Contributors

Pricing barrier options with simulations and sensitivity analysis with Greeks

by

Pricing barrier options with simulations and sensitivity analysis with Greeks

Shengyu ZHENG

In this article, Shengyu ZHENG (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023) explains the pricing of barrier options with Monte-Carlo simulations and the sensitivity analysis of barrier options from the perspective of Greeks.

Pricing of discretely monitored barrier options with Monte-Carlo simulations

With the simulation method, only the pricing of discretely monitored barrier options can be handled since it is impossible to simulate continuous price trajectories with no intervals. Here the method is illustrated with a down-and-out put option. The general setup of economic details of the down-and-out put option and related market information are presented as follows:

General setup of simulation for barrier option pricing

Similar to the simulation method for pricing standard vanilla options, Monte Carlo simulations based on Geometric Brownian Motion could also be employed to analyze the pricing of barrier options.

Figure 1. Trajectories of 600 price simulations.

With the R script presented above, we can simulate 6,000 times with the simprice() function from the derivmkts package. Trajectories of 600 price simulations are presented above, with the black line representing the mean of the final prices, the green dashed lines 1x and 2x standard deviation above the mean, the red dashed lines 1x and 2x derivation below the mean, the blue dashed line the strike level and the brown line the knock-out level.

The simprice() function, according to the documentation, computes simulated lognormal price paths with the given parameters.

With this simulation of 6,000 price paths, we arrive at a price of 0.6720201, which is quite close to the one calculated from the formulaic approach from the previous post.

Analysis of Greeks

The Greeks are the measures representing the sensitivity of the price of derivative products including options to a change in parameters such as the price and the volatility of the underlying asset, the risk-free interest rate, the passage of time, etc. Greeks are important elements to look at for risk management and hedging purposes, especially for market makers (dealers) since they do not essentially take these risks for themselves.

In R, with the combination of the greeks() function and a barrier pricing function, putdownout() in this case, we can easily arrive at the Greeks for this option.

Barrier option R code Sensitivity Greeks

Table 1. Greeks of the Down-and-Out Put

Barrier Option Greeks Summary

We can also have a look at the evolutions of the Greeks with the change of one of the parameters. The following R script presents an example of the evolutions of the Greeks along with the changes in the strike price of the down-and-out put option.

Barrier option R code Sensitivity Greeks Evolution

Figure 2. Evolution of Greeks with the change of Strike Price of a Down-and-Out Put

Evolution Greeks Barrier Price

Download R file to price barrier options

You can find below an R file (file with txt format) to price barrier options.

Download R file to price barrier options

Why should I be interested in this post?

As one of the most traded but the simplest exotic derivative products, barrier options open an avenue for different applications. They are also very often incorporated in structured products, such as reverse convertibles. It is, therefore, important to be equipped with knowledge of this product and to understand the pricing logics if one aspires to work in the domain of market finance.

Simulation methods are very common in pricing derivative products, especially for those without closed-formed pricing formulas. This post only presents a simple example of pricing barrier options and much optimization is needed for pricing more complex products with more rounds of simulations.

Related posts on the SimTrade blog

   ▶ All posts about Options

   ▶ Shengyu ZHENG Barrier options

   ▶ Shengyu ZHENG Pricing barrier options with analytical formulas

Useful resources

Academic articles

Broadie, M., Glasserman P., Kou S. (1997) A Continuity Correction for Discrete Barrier Option. Mathematical Finance, 7:325-349.

Merton, R. (1973) Theory of Rational Option Pricing. The Bell Journal of Economics and Management Science, 4:141-183.

Paixao, T. (2012) A Guide to Structured Products – Reverse Convertible on S&P500

Reiner, E.S., Rubinstein, M. (1991) Breaking down the barriers. Risk Magazine, 4(8), 28–35.

Rich, D. R. (1994) The Mathematical Foundations of Barrier Option-Pricing Theory. Advances in Futures and Options Research: A Research Annual, 7:267-311.

Wang, B., Wang, L. (2011) Pricing Barrier Options using Monte Carlo Methods, Working paper.

Books

Haug, E. (1997) The Complete Guide to Option Pricing. London/New York: McGraw-Hill.

Hull, J. (2006) Options, Futures, and Other Derivatives. Upper Saddle River, N.J: Pearson/Prentice Hall.

About the author

The article was written in June 2022 by Shengyu ZHENG (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023).

Asset Allocation

by

Asset Allocation

Akshit Gupta

This article written by Akshit GUPTA (ESSEC Business School, Grande Ecole Program – Master in Management, 2019-2022) explains asset allocation, a much-discussed topic in asset management.

Introduction

Asset allocation refers to the process of dividing an investment among different assets and, at a more integrated level, asset classes, sectors of the economy and geographical areas.

The allocation of an investor’s money across different assets can be analyzed according to different dimensions: investment objective, risk profile, and time horizon. The allocation process helps in finding a right balance between these dimensions and ultimately generates optimal returns in terms of expected return and risk. A key concept underlying asset allocation is diversification.

There are several assets in financial markets that the investor can use in his/her asset allocation. These asset classes include traditional assets like equities, bonds and cash, and alternative assets like real estates, commodities, and cryptocurrencies. Investors may also use combinations of such basic assets like mutual funds, exchange trade funds and more complex products like structured products.

Basics of asset allocation

Characteristics of investors

The characteristics of asset allocation for investors comes from its significant impact on the portfolio performance. Asset allocation decisions rely on input of the process: investment objective, risk profile, and time horizon.

Investment objective

The process of asset allocation impacts the financial objectives of the investor. If the investor has a low-risk appetite, he/she might be exposed to high degree of risk by investing in equities. Thus, such an investor should invest in safer assets such as bonds and fixed deposits to have a low-risk portfolio.

Risk Profile

The risk appetite of an investor determines the mix of different asset classes in a portfolio. Investors aiming for low risk should include a comparatively higher mix of risk less assets like bonds and real estate than equities.

Time horizon

The time horizon of an investment is also an important characteristic of the asset allocation process. Investors can either invest for a long-term time horizon or a short term depending on their investment objective.

Characteristics of assets

The characteristics of asset allocation comes from its significant impact on the portfolio performance. Asset allocation decisions can also rely on asset’s features such as: Expected returns, risk, and correlation.

Expected returns

The main focus of any investment in financial markets is to make maximum profits (returns) within a coherent risk level. Different asset classes have traditionally offered different returns, determined by their risk levels and market correlation. Generally, bonds have offered a lower long-term return as compared to the equity markets. Thus, investors aiming for higher returns should include an higher mix of these high return asset classes like equities than bonds.

Risk

Different asset classes have different characteristics and thus, different risk levels. The bonds market is generally considered less risky as compared to the equity markets. Thus, investment in bonds exposes the investor to a lower degree of risk than investing in equities.

Correlation

Different asset classes differ in their correlation which is also an important factor while deciding the optimal portfolio mix. It is possible that one asset class might be increasing in value whereas the other may be decreasing in value. For example, if the bonds markets are trending upwards, it is possible that the equity markets might be falling. Thus, by having an optimal mix of these asset, the investor can be compensated for the losses in equity markets with gains in the bond markets. Degree of correlation plays an important role in protecting the investor from downfalls in one asset class by compensating the losses with gains in other asset class.

Asset allocation processes

The asset allocation processes can be divided into two types: strategic asset allocation and tactical asset allocation.

Strategic asset allocation

Strategic asset allocation is a long-term investment strategy driven by long term market outlook and fundamental trends in the market. The strategy follows a top-down approach, and the investor generally looks at the macro level trends followed by trends in different asset classes to take the investment decisions. The investor following this allocation type generally has a pre-defined return expectation and risk tolerance levels and practices diversification to lower the risk. These investments are made in traditional assets like equities, bonds and cash assets but can also include alternative assets.

The investor follows a fixed objective which remains unchanged throughout the investment horizon. This can include a policy mix of investing 40% of portfolio in equities, 30% in bonds, 10% in real estate and remaining 20% in cash. As opposed to the tactical asset allocation, strategic asset allocation involves periodical rebalancing of the portfolio to get higher returns. If the investor diverges from the fixed objective, he/she must rebalance the portfolio to unify it with the original mix.

This strategy is suited to new or irregular investors who seek to generate returns at par with the market returns. The standard asset class suited for this strategy includes mutual funds, ETFs, blue-chip equities, bonds, fixed deposits, and real estate.

Tactical asset allocation

Tactical asset allocation involves actively investing in asset and securities to enhance portfolio returns by constantly rebalancing the portfolio and exploiting market anomalies. Even though the investor is following strategic asset allocation, the financial markets often present attractive buying or selling opportunities which can be exploited by tactical asset allocation to attain even higher returns. These opportunities can involve cyclical deviations in businesses, momentum trends and exploiting under valuations. However, these deviations from strategic allocation are often done carefully so as not to hinder the long-term objective.

The investment horizon in this strategy can be short or long depending on the investor’s preferences. However, the investor tries to generate higher returns and constantly rebalances the portfolio to achieve these returns by exploiting the market inefficiencies. Tactical asset allocation requires good understanding of the financial markets and is generally practiced by experienced investors with moderate to high risk tolerance.

Asset allocation over time

The investors deciding on the asset allocation process over time can follow different approaches, which includes:

Passive management: the buy-and-hold approach

In a passive asset management, the aim of the investor is to replicate the performance of a benchmark index. These investors can have lower risk appetite; thus, replications help to reduce the risk exposure for them. The investors following a passive approach can buy the individual components of the index by applying similar weights and invest with a moderate to long term time horizon in mind. The suitable asset classes for such investors can include mutual funds, exchange traded funds, index funds, etc.

Active management: dynamic asset allocation

In active asset management, the aim of the investor is to maximize the returns on the portfolio by actively investing in asset classes. The portfolio mix is frequently adjusted to capitalize on the short-term trends across different asset classes. The rebalancing decisions are based on business and economic cycles, momentum trends, relative valuations across different asset classes and macro factors like inflation, GDP growth, etc. The investor tries to beat the benchmark indices by dynamically trading in different asset classes and exploiting the market inefficiencies. They generally have high risk appetite and good knowledge about different asset classes. The suitable asset classes for such investors can include equities, commodities, and bonds.

Useful resources

US Securities and Exchange Commission (SEC) Asset Allocation

Related Posts

   ▶ Youssef LOURAOUI Systematic risk and specific risk

   ▶ Youssef LOURAOUI Portfolio

About the author

Article written in July 2022 by Akshit GUPTA (ESSEC Business School, Grande Ecole Program – Master in Management, 2019-2022).

Pricing barrier options with analytical formulas

by

Pricing barrier options with analytical formulas

Shengyu ZHENG

As is mentioned in the previous post, the frequency of monitoring is one of the determinants of the price of a barrier option. The higher the frequency, the more likely a barrier event would take place.

In this article, Shengyu ZHENG (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023) explains the pricing of continuously and discretely monitored barrier options with analytical formulas.

Pricing of standard continuously monitored barrier options

For pricing standard barrier options, we cannot simply apply the Black-Sholes-Merton Formula for the particularity of the barrier conditions. There are, however, several models available developed on top of this theoretical basis. Among them, models developed by Merton (1973), Reiner and Rubinstein (1991) and Rich (1994) enabled the pricing of continuously monitored barrier options to be conducted in a formulaic fashion. They are concisely put together by Haug (1997) as follows:

Knock-in and knock-out barrier option pricing formula

Knock-in barrier option pricing formula

Knock-in barrier option pricing formula

Pricing of standard discretely monitored barrier options

For discretely monitored barrier options, Broadie and Glasserman (1997) derived an adjustment that is applicable on top of the pricing formulas of the continuously monitored counterparts.

Let’s denote:

Knock-in barrier option pricing formula

The price of a discretely monitored barrier option of a certain barrier price equals the price of a continuously monitored barrier option of the adjusted price plus an error:

Knock-in barrier option pricing formula

The adjusted barrier price, in this case, would be:

Knock-in barrier option pricing formula

Knock-in barrier option pricing formula

It is also worth noting that the error term o(·) grows prominently when the barrier approaches the strike price. A threshold of 5% from the strike price should be imposed if this approach is employed for pricing discretely monitored barrier options.

Example of pricing a down-and-out put with R with the formulaic approach

The general setup of economic details of the Down-and-Out Put and related market information is presented as follows:

Knock-in barrier option pricing formula

There are built-in functions in the “derivmkts” library that render directly the prices of barrier options of continuous monitoring, such as calldownin(), callupin(), calldownout(), callupout(), putdownin(), putupin(), putdownout(), and putupout (). By incorporating the adjustment proposed by Broadie and Glasserman (1997), all barrier options of both monitoring methods could be priced in a formulaic way with the following function:

Knock-in barrier option pricing formula

For example, for a down-and-out Put option with the aforementioned parameters, we can use this function to calculate the prices.

Knock-in barrier option pricing formula

For continuous monitoring, we get a price of 0.6264298, and for daily discrete monitoring, we get a price of 0.676141. It makes sense that for a down-and-out put option, a lower frequency of barrier monitoring means less probability of a knock-out event, thus less protection for the seller from extreme downside price trajectories. Therefore, the seller would charge a higher premium for this put option.

Download R file to price barrier options

You can find below an R file (file with txt format) to price barrier options.

Download R file to price barrier options

Why should I be interested in this post?

As one of the most traded but the simplest exotic derivative products, barrier options open an avenue for different applications. They are also very often incorporated in structured products, such as reverse convertibles. It is, therefore, important to understand the elements having an impact on their prices and the closed-form pricing formulas are a good presentation of these elements.

Related posts on the SimTrade blog

   ▶ All posts about options

   ▶ Shengyu ZHENG Barrier options

   ▶ Shengyu ZHENG Pricing barrier options with simulations and sensitivity analysis with Greeks

Useful resources

Academic research articles

Broadie, M., Glasserman P., Kou S. (1997) A Continuity Correction for Discrete Barrier Option. Mathematical Finance, 7:325-349.

Merton, R. (1973) Theory of Rational Option Pricing. The Bell Journal of Economics and Management Science, 4:141-183.

Paixao, T. (2012) A Guide to Structured Products – Reverse Convertible on S&P500

Reiner, E.S., Rubinstein, M. (1991) Breaking down the barriers. Risk Magazine, 4(8), 28–35.

Rich, D. R. (1994) The Mathematical Foundations of Barrier Option-Pricing Theory. Advances in Futures and Options Research: A Research Annual, 7:267-311.

Wang, B., Wang, L. (2011) Pricing Barrier Options using Monte Carlo Methods, Working paper.

Books

Haug, E. (1997) The Complete Guide to Option Pricing. London/New York: McGraw-Hill.

Hull, J. (2006) Options, Futures, and Other Derivatives. Upper Saddle River, N.J: Pearson/Prentice Hall.

About the author

The article was written in July 2022 by Shengyu ZHENG (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023).

Barrier options

by

Barrier options

Shengyu ZHENG

In this article, Shengyu ZHENG (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023) explains barrier options which are the most traded exotic options in derivatives markets.

Description

Barrier options are path dependent. Their payoffs are not only a function of the price of the underlying asset relative to the option strike, but also depend on whether the price of the underlying asset reached a certain predefined barrier during the life of the option.

The two most common kinds of barrier options are knock-in (KI) and knock-out (KO) options.

Knock-in (KI) barrier options

KI barrier options are options that are activated only if the underlying asset attains a prespecified barrier level (the “knock-in” event). With the absence of this knock-in event, the payoff remains zero regardless of the trajectory of the price of the underlying asset.

Knock-out (KO) barrier options

KO barrier options are options that are deactivated only if the underlying asset attains a prespecified barrier level (the “knock-out” event). In the presence of this knock-out event, the payoff remains zero regardless of the trajectory of the price of the underlying asset.

Observation

The determination of the occurrence of a barrier event (KI or KO conditions) is essential to the ultimate payoff of the barrier option. In practice, the details of the KI or KO conditions are precisely defined in the contract (called “Confirmations” by the International Swaps and Derivatives Association (ISDA) for over-the counter (OTC) traded options).

Observation period

The observation period denotes the period where a barrier event (KI or KO) can be observed, that is to say, when the price of the underlying asset is monitored. There are three styles of observation period: European style, partial-period American style, and full-period American style.

  • European style: The observation period is only the expiration date of the barrier option.
  • Partial-period American style: The observation period is part of the lifespan of the barrier option.
  • Full-period American style: The observation period spans the whole period from the effective date to the expiration date of the barrier option.

Monitoring method

There are two typical types of monitoring methods in terms of the determination of a knock-in/knock-out event: continuous monitoring and discrete monitoring. The monitoring method is one of the key factors in determining the premium of a barrier option.

  • Continuous monitoring: A knock-in/knock-out event is deemed to take place if, at any time in the observation period, the knock-in/knock-out condition is met.
  • Discrete monitoring: A knock-in/knock-out event is deemed to occur if, at pre-specific times in the observation period, usually the closing time of each trading day, the knock-in/knock-out condition is met.

Barrier Reference Asset

For the most cases, the Barrier Reference Asset is the underlying asset itself. However, if specified in the contract, it can be another asset or index. It can also be other calculatable properties, such as the volatility of the asset. In this case, the methodology of calculating such properties should be clearly defined in the contract.

Rebate

For knock-out options, there could be a rebate. A rebate is an extra feature and it corresponds to the amount that should be paid to the buyer of the knock-out option in case of the occurrence of a knock-out event.

In-out parity relation for barrier options

Analogous to the call-put parity relation for plain vanilla options, there is an in-out parity relation for barrier options stating that a long position in a knock-in option plus a long position in a knock-out option with identical strikes, barriers, monitoring methods and maturity is equivalent to a long position in a comparable vanilla option. It could be stated as follows:

Knock-in knock-out barrier option parity relation

Where K denotes the strike price, T the maturity, and B the barrier level.

It is worth noting that this parity relation is valid only when the two KI and KO options are identical, and there is no rebate in case of a knock-out option.

Basic barrier options

There are four types of basic barrier options traded in the market: up-and-in option, up-and-out option, down-and-in option, and down-and-out option. “Up” and “down” denotes the direction of surpassing the barrier price. “In” and “out” depict the type of barrier condition, i.e. knock-in or knock-out. These four types of barrier features are available for both call and put options.

Up-and-in option

An up-and-in option is a knock-in option whose barrier condition is achieved if the underlying price arrives higher than the barrier level during the observation period.

Figure 1 illustrates the occurrence of an up-and-in barrier event for a barrier option with full-period American style and discrete monitoring (the closing time of each trading day).

Figure 1. Illustration of an up-and-in barrier option
Example of an up-and-in call option

Up-and-out option

An up-and-out option is a knock-out option whose barrier condition is achieved if the underlying price arrives higher than the barrier level during the observation period.

Figure 2. Illustration of an up-and-out option

Example of an up-and-out call option

Down-and-in option

A down-and-in option is a knock-in option whose barrier condition is achieved if the underlying price arrives lower than the barrier level during the observation period.

Figure 3. Illustration of a down-and-in option
Example of a down-and-in call option

Down-and-out option

A down-and-out option is a knock-out option whose barrier condition is achieved if the underlying price arrives lower than the barrier level during the observation period.

Figure 4. Illustration of a down-and-out option
Example of a down-and-out call option

Download R file to price barrier options

You can find below an R file to price barrier options.

Download R file to price barrier options

Trading of barrier options

Being the most popular exotic options, barrier options on stocks or indices have been actively traded in the OTC market since the inception of the market. Unavailable in standard exchanges, they are less accessible than their vanilla counterparts. Barrier options are also commonly utilized in structured products.

Why should I be interested in this post?

As one of the most traded but the simplest exotic derivative products, barrier options open an avenue for different applications. They are also very often incorporated in structured products, such as reverse convertibles. Knock-in/knock out conditions are also common features in other types of more complicated exotic derivative products.

It is, therefore, important to be equipped with knowledge of this product and to understand the pricing logics if one aspires to work in financial markets.

Related posts on the SimTrade blog

   ▶ All posts about options

   ▶ Shengyu ZHENG Pricing barrier options with analytical formulas

   ▶ Shengyu ZHENG Pricing barrier options with simulations and sensitivity analysis with Greeks

References

Academic research articles

Broadie, M., Glasserman P., Kou S. (1997) A Continuity Correction for Discrete Barrier Option. Mathematical Finance, 7:325-349.

Merton, R. (1973) Theory of Rational Option Pricing. The Bell Journal of Economics and Management Science, 4:141-183.

Paixao, T. (2012) A Guide to Structured Products – Reverse Convertible on S&P500

Reiner, E.S., Rubinstein, M. (1991) Breaking down the barriers. Risk Magazine, 4(8), 28–35.

Rich, D. R. (1994) The Mathematical Foundations of Barrier Option-Pricing Theory. Advances in Futures and Options Research: A Research Annual, 7:267-311.

Wang, B., Wang, L. (2011) Pricing Barrier Options using Monte Carlo Methods, Working paper.

Books

Haug, E. (1997) The Complete Guide to Option Pricing. London/New York: McGraw-Hill.

Hull, J. (2006) Options, Futures, and Other Derivatives. Upper Saddle River, N.J: Pearson/Prentice Hall.

About the author

The article was written in July 2022 by Shengyu ZHENG (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023).

Fama-MacBeth two-step regression method

by

Youssef_Louraoui

In this article, Youssef Louraoui (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022) presents the Fama-MacBeth two-step regression method used to test asset pricing models.

This article is structured as follow: we introduce the Fama-MacBeth testing method. Then, we present the mathematical foundation that underpins their approach. We conclude with a practical case study followed by a discussion on econometric issues.

Introduction

Risk factors are frequently employed to explain asset returns in asset pricing theories. These risk factors may be macroeconomic (such as consumer inflation or unemployment) or microeconomic (such as firm size or various accounting and financial metrics of the firms). The Fama-MacBeth two-step regression approach a practical way for measuring how correctly these risk factors explain asset or portfolio returns. The aim of the model is to determine the risk premium associated with the exposure to these risk factors.

The first step is to regress the return of every asset against one or more risk factors using a time-series approach. We obtain the return exposure to each factor called the “betas” or the “factor exposures” or the “factor loadings”.

The second step is to regress the returns of all assets against the asset betas obtained in Step 1 using a cross-section approach. We obtain the risk premium for each factor. Then, Fama and MacBeth assess the expected premium over time for a unit exposure to each risk factor by averaging these coefficients once for each element.

Mathematical foundations

We describe below the mathematical foundations for the Fama-MacBeth two-step regression method.

Step 1: time-series analysis of returns

The model considers the following inputs:

  • The return of N assets denoted by Ri for asset i observed over the time period [0, T].
  • The risk factors denoted by F1 for the market factor influencing the asset returns.

For each asset i from 1 to N, we estimate the following parameters:

img_SimTrade_Fama_MacBeth_time_series

From this model, we obtain a series of coefficients: αi which is the risk premium for asset i and the βi, F1 associated to the market risk factor.

Figure 1 represents for a given asset the regression of a return with respect to the market factor (as in the CAPM). The slope of the regression line corresponds to the market beta of the regression.

Figure 1 Time-series regression.
Time-series regression Source : computation by the author.

The econometric issues (estimation bias, heteroscedasticity, and autocorrelation) related to the model are discussed in more details in the econometric limitation section.

Step 2: cross-sectional analysis of returns

For each period t from 1 to T, we estimate the following linear regression model:

img_SimTrade_Fama_MacBeth_cross_section

Figure 2 plots for a given period the cross-sectional returns and betas for a given point in time.

Figure 2 represents for a given period the regression of the return of all individual assets with respect to their estimated individual market beta.

Figure 2. Cross-sectional regression.
Time-series regression Source: computation by the author.

We average the gamma obtained for each data point. This is the way the Fama-MacBeth method is used to test asset pricing models.

Empirical study by Fama and MacBeth (1973)

Fama-MacBeth performed a second time the cross-sectional regression of monthly stock returns on the equity betas computed on the initial workings to account for the dynamic nature of stock returns, which help to compute a robust standard error to gauge the level of error and assess if there is any heteroscedasticity in the regression. The conclusion of the seminal paper suggests that the beta is “dead”, in the sense that it cannot explain returns on its own (Fama and MacBeth, 1973).

New empirical study

We downloaded a sample of end-of-month stock prices of large firms in the US economy over the period from March 31, 2016, to March 31, 2022. We computed monthly returns. To represent the market, we chose the S&P500 index.

We then applied the Fama-MacBeth two-step regression method to test the market factor (CAPM).

Figure 3 depicts the computation of average returns and the betas and stock in the analysis.

Figure 3. Computation of average returns and betas of the stocks.
img_SimTrade_Fama_MacBeth_method_4 Source: computation by the author.

Figure 4 represents the first step of the Fama-MacBeth regression. We regress the average returns for each stock with their respective betas.

Figure 4. Step 1 of the regression: Time-series analysis of returns
img_SimTrade_Fama_MacBeth_method_1 Source: computation by the author.

The initial regression is statistically evaluated. To describe the behaviour of the regression, we employ a t-statistic. Since the p-value is in the rejection area (less than the significance limit of 5 percent), we can deduce that the market factor can at first explain the returns of an investor. However, as we are going deal in the later in the article, when we account for a second regression as formulated by Fama and MacBeth, the market factor is not capable of explaining on its own the return of asset returns.

Figure 5 represents Step 2 of the Fama-MacBeth regression, where we perform for a given data point a regression of all individual stock returns with their respective estimated market beta.

Figure 5. Step 2: cross-sectional analysis of return.
img_SimTrade_Fama_MacBeth_method_2 Source : computation by the author.

Figure 6 represents the hypothesis testing for the cross-sectional regression. From the results obtained, we can clearly see that the p-value is not in the rejection area (at a 5% significance level), hence we cannot reject the null hypothesis. This means that the market factor fails to explain properly the behaviour of asset returns, which undermines the validity of the CAPM framework. These results are in line with the Fama-MacBeth paper (1973).

Figure 6. Hypothesis testing of the cross-sectional regression.
img_SimTrade_Fama_MacBeth_method_1 Source: computation by the author.

Excel file for the Fama-MacBeth two-step regression method

You can find below the Excel spreadsheet that complements the explanations covered in this article to apply the Fama-MacBeth two-step regression method.

Download the Excel file to perform a Fama-MacBeth two-step regression method

Econometric issues

Errors in data measurement

Because regression uses a sample instead of the entire population, a certain margin of error must be accounted for since the authors derive estimated betas for the sample.

Asset return heteroscedasticity

In econometrics, heteroscedasticity is an important concern since it results in unequal residual variance. This indicates that a time series exhibiting some heteroscedasticity has a non-constant variance, which renders forecasting ineffective because the time series will not revert to its long-run mean.

Asset return autocorrelation

Standard errors in Fama-MacBeth regressions are solely corrected for cross-sectional correlation. This method does not fix the standard errors for time-series autocorrelation. This is typically not a concern for stock trading, as daily and weekly holding periods have modest time-series autocorrelation, whereas autocorrelation is larger over long horizons. This suggests that Fama-MacBeth regression may not be applicable in many corporate finance contexts where project holding durations are typically lengthy.

Limitation of CAPM

Roll: selection of the appropriate market index

For the CAPM to be valid, we need to determine if the market portfolio is in the Markowitz efficient curve. According to Roll (1977), the market portfolio is not observable because it cannot capture all the asset classes (human capital, art, and real estate among others). He then believes that the returns cannot be captured effectively and hence makes the market portfolio, not a reliable factor in determining its efficiency.

Furthermore, the coefficients are sensitive to the market index chosen for the study. These shortcomings must be taken into account when assessing other CAPM studies.

Fama-MacBeth: Stability of the coefficients

The stability of the beta across time is difficult. Fama-MacBeth attempted to address this shortcoming by implementing its innovative approach. However, some points need to be addressed:

When betas are computed using a monthly time series, the statistical noise of the time series is considerably reduced as opposed to shorter time frames (i.e., daily observation).

Constructing portfolio betas makes the coefficient much more stable than when assessing individual betas. This is due to the diversification effect that a portfolio can achieve, reducing considerably the amount of specific risk.

Why should I be interested in this post?

Fama-MacBeth made a significant contribution to the field of econometrics. Their findings cleared the way for asset pricing theory to gain traction in academic literature. The Capital Asset Pricing Model (CAPM) is far too simplistic for a real-world scenario since the market factor is not the only source that drives returns; asset return is generated from a range of factors, each of which influences the overall return. This framework helps in capturing other sources of return.

Related posts on the SimTrade blog

▶ Jayati WALIA Capital Asset Pricing Model (CAPM)

▶ Youssef LOURAOUI Security Market Line (SML)

▶ Youssef LOURAOUI Origin of factor investing

▶ Youssef LOURAOUI Factor Investing

Useful resources

Academic research

Brooks, C., 2019. Introductory Econometrics for Finance (4th ed.). Cambridge: Cambridge University Press. doi:10.1017/9781108524872

Fama, E. F., MacBeth, J. D., 1973. Risk, Return, and Equilibrium: Empirical Tests. Journal of Political Economy, 81(3), 607–636.

Roll R., 1977. A critique of the Asset Pricing Theory’s test, Part I: On Past and Potential Testability of the Theory. Journal of Financial Economics, 1, 129-176.

American Finance Association & Journal of Finance (2008) Masters of Finance: Eugene Fama (YouTube video)

Business Analysis

NEDL. 2022. Fama-MacBeth regression explained: calculating risk premia (Excel). [online] Available at: [Accessed 29 May 2022].

About the author

The article was written in May 2022 by Youssef LOURAOUI (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022).

Introduction to Hedge Funds

by

Youssef_Louraoui

In this article, Youssef LOURAOUI (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022) elaborates on the concept of Hedge Funds. Hedge funds are a type of asset class that differs from standard fixed-income and equities investments in terms of risk/return profile.

The structure of this article is as follows: First, we will define a hedge fund. Second, we provide a historical perspective on the first known hedge fund. Third, we will discuss hedge fund fees. Fourth, we discuss the conventional long-short strategy and provide an overview of the major hedge fund strategies. And finally, we end by discussing the economic importance of hedge funds.

Introduction

There is no straightforward definition of a hedge fund. Simply said, a hedge fund is an investment vehicle that aims to create performance by employing a variety of complex trading strategies. When the first hedge fund was introduced, the term “hedge” referred to lowering risk by investing in both long and short positions at the same time.

Hedge funds are exempted from the financial regulations that apply to other investment vehicles such as mutual funds. On the one hand, hedge funds have a lot of freedom to implement their investment strategy and face minimal disclosure rules. Hedge funds have the freedom to utilize leverage using derivatives products. On the other hand, hedge funds are restricted in the way they raise money from investors. Hedge fund investors must be “accredited investors,” which means they must have a particular amount of financial wealth and/or financial education to invest. Hedge funds have also been subject to a non-solicitation restriction, which means they are not allowed to advertise or aggressively seek individuals for investment.

According to the Security Exchange Commission (SEC, ), the governmental branch for regulated financial markets in the US, a hedge fund can be defined as follows:

“Hedge fund’ is a general, non-legal term used to describe private, unregistered investment pools that traditionally have been limited to sophisticated, wealthy investors. Hedge funds are not mutual funds and, as such, are not subject to the numerous regulations that apply to mutual funds for the protection of investors – including regulations requiring a certain degree of liquidity, regulations requiring that mutual fund shares be redeemable at any time, regulations protecting against conflicts of interest, regulations to assure fairness in the pricing of fund shares, disclosure regulations, regulations limiting the use of leverage, and more.” (SEC)

The first hedge fund: Jones

In 1949, Alfred Winslow Jones is said to have founded the first professional hedge fund and is regarded as the “father of the hedge fund industry”. He set up the fund as a limited partnership, with the hedge fund manager providing significant initial capital and a few significant investors. The fund’s principal strategy was to use a long/short method, the fund being long on undervalued securities and short on overvalued securities. Jones based his investment approach on stock picking (he believed he lacked market timing skills). Hedge funds’ main idea is that they can use leverage to boost returns in both directions.

From 1955 to 1965, Jones is reported to have achieved a 670% return on his hedge fund by taking both long and short positions. Before Jones, short selling had been popular for a long time, but he realized that by balancing long and short positions, he could be relatively immune to overall market changes while benefiting from the relative outperformance of his long positions against his short positions. The performance of Jones’s fund is shown in Figure 1 about the Dow Jones Industrials index used as a benchmark and Fidelity’s highest performing mutual fund. Over the 1960-65 period, the fund managed to multiply its return by a factor of four, which is higher than the best performing mutual fund (Fidelity Trend Fund) and the Dow-Jones industrials.

Figure 1. Alfred Winslow Jones’s hedge fund performance between 1960-65.
img_SimTrade_jones_performance
Source: “The Jones Nobody Keeps Up With” (Fortune, 1966).

Development of hedge funds

Interest in hedge funds grew after Fortune magazine published Jones’s results in 1966, and the Securities and Exchange Commission (SEC) listed 140 hedge funds in 1968. As institutional investors began to embrace hedge funds in the 1990s, the hedge fund industry saw a huge spike in interest. Hedge funds with billions of dollars under management were typical in the 2000s, with total hedge fund assets reaching a peak of nearly $2 trillion before the global financial crisis of 2008, dropping during the crisis, and recently reached a new peak.

Hedge funds’ aggregate positions are much larger than their assets under management due to their leverage, and their trading volume is a much larger part of the aggregate trading volume than their relative position sizes due to their high turnover, so hedge fund trading now accounts for a significant portion of all trading. Given a limited demand for liquidity, there is a limited amount of profit to be made and a limited requirement for active investment in an optimally inefficient market, the quantity of capital committed to hedge funds cannot keep expanding.

Hedge funds fees

Among the most frequent fees in the hedge fund industry, we can name the following:

Management fee

Management fee represents the fees that the hedge funds collect to run their operations (salaries, infrastructure, etc.). The management fee is usually about 3%

Performance fee

The performance fee is a compensation when the hedge fund achieves a certain level of performance. This threshold, called the hurdle rate, represents the minimum performance that a hedge fund has to achieve to charge an incentive fee. This motivates the hedge fund manager to perform and to align its interest with its clients’ interests. Beyond the hurdle rate, the outperformance is shared between the hedge fund manager (20%) and the clients (80%).

The high water mark (HWM) provision is a mechanism where the hedge fund will only charge performance fees if it manages to deliver returns above the returns of the previous period. If the hedge fund is down 50%, the performance achieved to recover the losses (100% won’t be subject to performance fees). Only after recovering entirely from the drawdown, the hedge fund can be entitled to earn the performance fee.

A classic hedge fund strategy: the long-short strategy

The long-short strategy is the strategy implemented by the first hedge fund (Alfred Winslow Jones fund). According to Credit Suisse, long-short equity funds engage in both the long and short sides of the equity markets, to diversify or hedge across sectors, regions, and market capitalizations. Managers can switch from value to growth, from small to medium to large capitalization equities, and from net long to net short positions. Managers can also trade stock futures and options, as well as equity-related instruments and debt, and form more concentrated portfolios than classic long-only equity funds.

To illustrate a long-short strategy, we create a hedge fund portfolio based on two stocks from the US equity market. We pick one overvalued stock and one undervalued stock based on their price-to-earnings (P/E) ratio. We chose for this purpose Twitter (overvalued) and Pfizer (undervalued). We download a time series of three-month worth of data for two stocks (Twitter and Pfizer) and the S&P500 index.

Figure 2 represents the regression of the returns of the simulated hedge fund portfolio on the S&P500 index. We can appreciate a null slope (0.0936) of the regression indicating the low correlation of the hedge fund with the market represented by the S&P500 index. This strategy is market-neutral, meaning that the portfolio is not correlated directly with the market fluctuations. The performance of a zero-beta portfolio would be derived from the alpha, a key metric in the portfolio management industry.

Figure 2. Regression of the hedge fund return on the S&P500 market index.
Hedge fund portfolio regression
Source: computation by the author (data: Bloomberg).

We compute the return and volatility of each security and the market index as a starting point. We also determine the correlation of the stocks to the market index. For the short position (Twitter), the sign of the correlation inverts of the sign. We compute an equally-weighted portfolio composed of two stocks: a long position on Pfizer and a short position on Twitter. This portfolio delivered a return of 0.27%, which is better than the broader stock index return over the same period (-0.22%).

Figure 3 depicts the return of the hedge fund portfolio relative to the market index return. From the analysis, the long-short strategy managed to outperform the S&P500 market index by 49 basis points. Even if the market is in a bearish setting, the strategy managed to deliver positive returns as the short position helps to be uncorrelated the return of the hedge fund from the market return.

Figure 3. Return of the hedge fund relative to the S&P500 market index.
Long short strategy performance
Source: computation by the author (data: Bloomberg).

You can download below the Excel file below which gives the details of the computation of the long-short strategy example.

Excel file for the long-short startegy example

Hedge fund role in economy

Hedge funds, for example, are frequently criticized in the media. Companies, for example, dislike seeing their shares shorted because it indicates a belief that the company’s share price will fall. Short sellers, including hedge funds, are sometimes blamed for a company’s problems, even though the stock price is usually falling due to the company’s poor financial condition, not because of any other source.

Hedge funds, in general, serve several important functions in the economy. First, they improve market efficiency by gathering information about businesses and incorporating it into prices through their trades. Because the capital market is the tool used to allocate resources in the economy, increased efficiency can improve real economic outcomes. Companies with good growth prospects see their share prices rise when markets are efficient, allowing them to raise capital and fund new projects. Companies that produce goods and services that are no longer required to see their share prices fall and the factories may be repurposed for more productive purposes, possibly leading to a merger. Furthermore, when share prices reflect more information and are more efficient, CEO decisions may improve, and they may be more prudent if active investors are monitoring them. Hedge funds also serve as a source of liquidity for other investors who need to buy or sell (e.g., to smooth out their consumption), hedge or buy insurance, or simply enjoy certain types of securities. Finally, hedge funds offer investors another source to diversify their returns.

Why should I be interested in this post?

As an investor, hedge funds may provide an opportunity to diversify its global portfolios. Including hedge funds in a portfolio can help investors obtain absolute returns that are uncorrelated with typical bond/equity returns.

For practitioners, learning how to incorporate hedge funds into a standard portfolio and understanding the risks associated with hedge fund investing can be beneficial.

Understanding if hedge funds are truly providing “excess returns” and deconstructing the sources of return can be beneficial to academics. Another challenge is determining whether there is any “performance persistence” in hedge fund returns.

Getting a job at a hedge fund might be a profitable career path for students. Understanding the market, the players, the strategies, and the industry’s current trends can help you gain a job as a hedge fund analyst or simply enhance your knowledge of another asset class.

Useful resources

Academic research

Pedersen, L. H., 2015. Efficiently Inefficient: How Smart Money Invests and Market Prices Are Determined. Princeton University Press.

Business Analysis

Wikipedia Alfred Winslow Jones

Fortune (2015) The Jones Nobody Keeps Up With (Fortune, 1966).

SEC Mutual Funds and Exchange-Traded Funds (ETFs) – A Guide for Investors.

SEC Selected Definitions of “Hedge Fund”

Credit Suisse Hedge fund strategy

Credit Suisse Hedge fund performance

Credit Suisse Long-short strategy

Credit Suisse Long-short performance benchmark

Related posts on the SimTrade blog

   ▶ Shruti CHAND Financial leverage

   ▶ Akshit GUPTA Initial and maintenance margins in futures contracts

   ▶ Akshit GUPTA Hedge funds

About the author

The article was written in June 2022 by Youssef LOURAOUI (Bayes Business School, MSc. Energy, Trade & Finance, 2021-2022).

A quick presentation of the Asset Management field…

by

A quick presentation of the Asset Management field…

Louis DETALLE

In this article, Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023) explains what does an Asset Management company consists in.

What does Asset Management consist in?

Asset management is a financial activity whose objective is to create, manage, grow and maximize the benefits of financial products or investments entrusted by companies or individual investors. Asset management therefore consists in managing a client portfolio and increasing its profitability by balancing expected returns and risks in order to achieve previously defined objectives.

When thinking about asset management, companies such as Allianz, Amundi, AVIVA or Natixis Investment Managers could be quoted as examples of Asset Management companies.

What are the main clients of Asset Managers?

The main clients of asset management companies are :

– Companies wishing to invest their cash surpluses;
– Pension funds and mutual insurance companies;
– Financial institutions investing for their own account;
– Banks and insurance companies that distribute financial products to their clients (retail, private and corporate banking).

Two main types of management

Management under mandate

The company manages the account of a single client or a group of clients who have delegated the management of the fund to it. All of the fund’s assets belong to one person or to a small number of people,

Collective management

A fund with a large number of investors and units. It is managed according to the same strategic orientation corresponding to the profile adapted to these investors.

What does an asset manager work on?

The day-to-day work consists mainly of assessing how the previous day’s transactions and market movements have affected the portfolio’s risk profile in terms of liquidity, credit and market.

Another key aspect of this job is the development, adaptation and improvement of quantitative portfolio risk analysis tools. Other tools to assist investment decisions, to monitor developments in financial research in terms of risk and to analyze macroeconomic news require more specific attention and are therefore more complex to implement.

Useful resources

Thinking ahead Institute The world’s largest asset managers – 2021

Related posts on the SimTrade blog

Understand the importance of data providers and how they influence global finance…

About the author

The article was written in May 2022 by Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023).

Reuters

by

Reuters

Louis DETALLE

In this article, Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023) explains everything there is to know about Reuters, the international giant in the data-providing market…

Quick presentation of the company

Thomson Reuters is a leading provider of business information services. As one of the main competitors of Bloomberg, their products include highly specialized information-enabled software and tools for legal, tax, accounting and compliance professionals combined with the world’s most global news service – Reuters.

Reuters is organized in 5 different business units:

Legal Professionals: This business unit serves law firms and governments with research products, focusing on intuitive legal research powered by emerging technologies and integrated legal workflow solutions that combine content, tools and analytics.

Corporates: Designed for corporate customers from small businesses to multinational organizations, this business unit provides its clients with a full suite of content-enabled technology solutions for in-house legal, tax, regulatory, compliance and IT professionals.

Tax & Accounting Professionals: This business provides its customers with research that focuses on intuitive tax offerings and automating tax workflows.

Reuters News: Supplies business, financial and global news to the world’s media organizations, professionals and news consumers through their many platforms.

Global Print: Provides legal and tax information primarily in print format to customers around the world.

Type of people working at Bloomberg (types of jobs)

Nearly 2/3 of Reuters’ employees work in the US, the remaining third working in Asia and in Europe. The careers available at Reuters are therefore numerous and very diverse.

Indeed, the profiles needed by Reuters consists in legal professionals, corporate professionals, tax & accounting professionals and journalists. Thomson Reuters also employs many software designers to help design the Reuters’ terminals, as well as sectorial legal and corporate specialists in order to provide precise and adequate analysis.

Main competitors

As Thomson Reuters’ activities are very diverse, we will classify the main competitors of the firm in respect to the activities.

For Thomson Reuters’ business that consists of software-design, Bloomberg LLP is the most natural competitor in this space with its very famous Bloomber Terminal. The terminal business is built on a fantastic technology platform that provides comprehensive financial information. There are other competitors, such as Dow Jones Industrial Average FX Trader, which have specialized in one type of industry whereas Reuters and Bloomberg remain generalists.

Reuters’ editorial branch’s main competitors would be Bloomberg News, the Financial Times (FT), the Wall Street Journal, and other traditional financial news companies. The same goes for their TV/radio operation (their competitor would be CNBC).

Use of data in financial markets

The explosion of financial data, enabled by the Internet tremendous potential, caused an explosion of demand for financial data. As evidenced in 2006 by the British mathematician and Tesco marketing mastermind Clive Humby’s quote, “Data is the new oil”, the data market seems to be limitless.

In addition, as Bloomberg acquires many of his competitors, such as BNA and BusinessWeek, this contributes to curbing the number of data providers and improving the monopoly of Bloomberg on the data-providing market. Reuters struggles to keep up the pace of its competitor which is very well established in this market.

Useful resources

Bloomberg

Reuters

Related posts on the SimTrade blog

   ▶ Louis DETALLE Understand the importance of data providers and how they influence global finance…

   ▶ Louis DETALLE Bloomberg

About the author

The article was written in March 2022 by Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023).

Branding and marketing in the financial services sector

by

Branding and marketing in the financial services sector

Samantha MARCUS Branding and Marketing in Financial Services

In this article, Samantha MARCUS (ESSEC Business School, Semester Exchange BBA, 2022) explains the changes and trends in branding and marketing in the financial services sector.

While generally overlooked, the messaging and branding of a financial institution is becoming increasingly more important, and financial institutions are facing pressure to act.

Financial service marketing uses various strategies and branding techniques to drive awareness and create brand loyalty. Unlike traditional tangible products, financial service providers must plant their brand in their consumers’ mind in any way they can because it is generally not a tangible product they are selling. The customer experience is extremely important in the financial services sector, and this is where branding and marketing play in. While the marketing approaches of financial products is oftentimes different than that of retail or consumer packaged goods, the marketing of financial services primarily utilizes two methods: traditional marketing and digital marketing

Traditional marketing

In the past, financial institutions have relied on marketing tactics such as word of mouth advertising, TV ads, radio and print marketing. As the consumer in the financial services sector is changing, these tactics are not as effective in this industry as they once were.

Digital marketing

In 2022, financial service providers are being pushed to step up their digital marketing efforts. Whether it’s through optimizing your firm’s SEO, creating more personalized algorithms or being active on social media platforms, there are various efforts in digital marketing happening in the financial services industry. The financial services sector is leaning more into digital marketing and this strategy is beginning to show more returns in terms of reaching new consumers and driving brand loyalty.

Why are branding and marketing important in financial services?

Retail banks, investment banks, brokerages, credit card companies, and many more financial service institutions can benefit from branding and marketing. Now more than ever, the younger generations are not trusting financial service institutions with about 92% of millennials claiming they do not trust banks at all (Financeography.com, 2016). Branding and marketing in this sector are so important as they can build this trust with consumers. Traditionally, financial service providers did not have to focus as much on branding and marketing because their services were deemed a necessity and consumers would normally approach them. This is all changing now for a variety of reasons that make this outdated mindset ineffective and dangerous. One of the largest reasons that it’s important for financial service providers to utilize marketing is the commoditization of financial products; standardization has made it harder for providers to differentiate their products as there are now more options than ever before. In addition, disruptive financial technology (like blockchains/cryptos) is changing the financial services sector and decentralizing the control, therefore marketing and grasping consumer awareness is more vital than ever. Lastly, as our world becomes increasingly more digitized, consumers are expecting personalized, digitized experiences regardless of the industry.

Trends for financial services marketing

The Rise of Personalization

As the financial service industry becomes increasingly more saturated and decentralized, personalization within marketing strategies offers a way for firms to differentiate themselves and shift the focus to a customer centric approach. Traditionally, banking and financial services have been more focused on their products rather than their consumers, however this is all changing. As within any marketing approach, when financial service providers have a grasp on their audience and demographic, they are better able to appeal to the wants and needs of this consumer and it even begins to become part of their brand. This approach is becoming increasingly more popular, and it helps create more personalized relationships which facilitate customer loyalty which is crucial in the financial services industry.

Digitalization

As there is an emphasis on mobile technology and e-commerce now more than ever, firms are adjusting their marketing strategies to be more digitalized and more customer centric as mentioned above. Many consumers now prefer to manage their finances online, so it only makes sense that digitalization is a priority. Consumers want to manage their money, pay their bills, and buy the things that they need to on their own terms, so it is important that financial brands message this in their marketing strategies as well as process the right technology to cater to this need. Whether it is through marketing techniques such as pay-per-click advertising, email marketing, search engine optimization or activity on social media, financial institutions must push to develop their brand online in order to be noticed amongst competitors.

Intriguing social media content

In the financial service industry, creating impactful content is now being discussed. Video content can be utilized as a great marketing took and form of content as firms can utilize videos to create video courses or webinars to help their target audience understand their products and the more complex financial concepts. It is also a trend to utilize content by showing in your social media posts your firms services and your specialties.

Using personal stories to build a brand

Since the modern consumer has changed especially in the financial sector, product-focused, cold, and impersonal branding does not cut it anymore. Consumers are less trusting of financial service providers so financial brands must find a way to capture their consumer’s attention and create a brand that they trust. While traditionally financial services did not place much emphasis on the human element, there is increasing pressure for financial brands to let this side shine through in their marketing. Is there an interesting story within your company? Is there a personable employee to tell a compelling finance story or explain events in the industry? The question is now: how can a firm use marketing to create trust with their consumers?

Key concepts

Branding

Branding is the personality of a brand. Branding can include everything from your logo to your mission statement; branding is how you define your business. Branding goes beyond just the color of your website or the style of your font, but rather it is how you tell a story and how you draw the attention of your target audience. Branding is how you make a connection with your target customers.

Marketing

While oftentimes people get branding and marketing get confused, there are fundamental differences between the two. Marketing is how a company positions their product based on their brand strategy. Marketing identifies a target market, uses the optimal tactics and segment markets to win over a bigger market share. Branding is all about knowing your company’s story while marketing is more focused on knowing who your target customers are.

Relevance to SimTrade Certificate

The SimTrade certificate allows students to increase their knowledge of financial markets, but it is also important to look at the business behind how financial institutions are able to thrive. Marketing and branding are a large part in this and how firms stay competitive.

Final thoughts

Marketing in financial institutions’ sector is very interesting because it is a quickly evolving area and institutions are experiencing pressure to act in order to sustain their businesses and keep their customers. Financial service marketing is so different from what we know as traditional marketing and advertising as it is not a tangible product, but it is peoples’ financials and essentially their life; this adds a lot of importance on winning over consumers loyalty and trust. As the modern consumer has more options now than ever before, financial firms are placing more importance on marketing and shifting their strategies from a product-obsessed mindset to a customer-focused mindset. Financial firms and their marketing teams must look into getting the right marketing technology to support their consumer centric marketing initiatives. There is so much opportunity to build trustworthy brands and improve customer’s experience through carefully crafted messaging and the right technology. The correct marketing provides a unique opportunity for financial institutions to differentiate themselves in this evolving marketing.

Related posts on the SimTrade blog

▶ Cynthia LIN Financial products marketing in neobanks

▶Ashima MALIK Financial products marketing

Useful resources

Academic books

Heding, T., Knudtzen, C. F., & Bjerre, M. (2009). Brand management: Research, theory and practice. Routledge.

Kapferer, J. N. (2012). The new strategic brand management: Advanced insights and strategic thinking. Kogan Page Publishers.

Business resources

Financeography.com (November 30, 2016) 92% of Millennials Do Not Trust Financial Institutions with Money Matters

O8 Agency for Marketing Financial Services Marketing: Everything You Need to Know

Templafy blog Industry Branding Series: Branding Financial Services

Purpose brand A corporate ESG content strategy puts brands at a competitive advantage.

About the author

The article was written in May 2022 by Samantha MARCUS (ESSEC Business School, Semester Exchange BBA, 2022).

The Paris Agreement

by

The Paris Agreement

Anant Jain

In this article, Anant JAIN (ESSEC Business School, Grande Ecole Program – Master in Management, 2019-2022) talks the Paris Agreement.

Introduction

The Paris Agreement is a global agreement that intends to keep global average temperatures below 2 degrees Celsius above pre-industrial levels by the end of the 21st century, with efforts to keep it below 1.5 degrees.

The Paris Agreement was drafted during the Conference of the Parties (COP 21) of the United Nations Framework Convention on Climate Change (UNFCCC21) and signed on December 12, 2015. The agreement was ratified on April 22, 2016, which was recognized as Earth Day by the United Nations, and was signed by all 196 UNFCCC members. In June 2017, President Donald Trump announced that the United States would withdraw from the Paris Agreement, claiming that it was not in the country’s best interests to do so.

Greenhouse gas emissions are considered as the primary cause of global warming.
To accomplish the agreement’s objectives, scientists have agreed that global greenhouse gas emissions must be reduced. As a result, the 20/20/20 targets were established: a 20% reduction in carbon dioxide (CO2) emissions, a 20% increase in renewable energy market share, and a 20% increase in energy efficiency through current technology such as insulation. The signatories are obligated to put efforts through Nationally Determined Contributions (NDCs), and to continue to do so in the future. This includes the duty to report on national emissions and decarbonization initiatives on a regular basis.

To keep global warming to a maximum of two degrees Celsius by 2100, scientists agree that the world will need to become carbon neutral by 2050. The International Panel on Climate Change (IPCC) issued a study in October 2018 warning that in order to meet the lower 1.5-degree objective, emissions must be reduced by 40-60% from 2010 levels by 2030, with net zero by 2050. To meet the less ambitious 2-degree objective, emissions must be reduced by 25%. Failure to do either will result in irreversible climate change beginning around 2030, according to the paper. According to the IPCC, if current levels of (in)activity continue, the 2-degree target will most likely be met by 2030, with global warming of 3 degrees by the end of the century becoming increasingly likely. The IPCC also warned in September 2019 that unless the world takes action now, sea levels will increase by at least one meter by 2100.

According to studies, CO2 produced by burning fossil fuels for power, heating, cooling, and transportation is the primary cause of global warming. Carbon dioxide levels in the atmosphere in 2017 were last seen on Earth three million years ago, according to research from the Potsdam Institute for Climate Impact. Before humans originated, the average surface temperature was 2-3 degrees Celsius higher than pre-industrial levels, and the average sea level was up to 25 meters higher than it is today during the Pliocene Era.

The Working Process

The Paris Agreement’s implementation necessitates economic and societal transformations based on the best available knowledge. The Paris Agreement is structured on a five-year cycle in which countries take more ambitious climate action each year. Countries must submit their climate action plans, known as Nationally Determined Contributions (NDCs) by 2020.

NDCs

Countries need to establish the steps that they will take to alleviate greenhouse gas emissions in their NDCs to align with the Paris Agreement’s agendas. Countries also outline the activities they plan to take to build resilience and adapt to the effects of rising temperatures.

Long-Term Planning

The Paris Agreement called for nations to draft and submit long-term low-carbon development strategies by 2020 in order to effectively define their efforts toward the long-term goal (LT-LEDS).

The long-term vision offered by LT-LEDS is beneficial to Nationally Determined Contributions (NDCs). They are not required, unlike NDCs. Irrespective, they place the NDCs in the context of countries’ long-term planning and development goals, giving them a vision and direction for future development.

How are countries supporting one another?

The Paris Agreement establishes a framework for assisting developing countries with financial, technical, and capacity-building support.

Finance

The Paris Agreement maintains that affluent countries should lead in providing financial support to less developed and vulnerable countries, while also encouraging voluntary contributions from other Parties for the first time. Since large financial resources are required to adjust to the negative effects of climate change and mitigate its consequences, it is imperative to adapt climate finance (financing that supports projects to contribute to climate change).

Technology

The Paris Agreement outlines a goal of fully implementing technological development and transfer in order to improve climate change resilience while also lowering greenhouse gas emissions (GHG) emissions. Through its policy and implementation arms, the mechanism is increasing technology development and transfer.

Capacity-Building

Many of the issues posed by climate change are beyond the capabilities of many developing countries. As a result, the Paris Agreement places a strong emphasis on developing nations’ climate-related capacity-building efforts and calls on all wealthy countries to increase their assistance for such efforts.

How are we tracking progress?

Countries adopted a more transparent framework with the Paris Agreement known as the Enhanced Transparency Framework (or ETF) to report information. Starting in 2024, countries will be required to report honestly on their activities and progress in climate change mitigation, adaptation, and support offered or received under the ETF. It also establishes worldwide protocols for the examination of reports provided.

The data from the ETF will be incorporated into the Global Stocktake, which will assess how far we’ve progressed toward our long-term climate goals. This will lead to recommendations for countries to establish more ambitious targets in the next phase.

What have we achieved so far?

Even though massive improvements in climate change action are required to reach the Paris Agreement’s goals, low-carbon solutions and new markets have already emerged in the years after it went into effect. A growing number of governments, regions, cities, and corporations are setting carbon neutrality goals. Zero-carbon solutions are becoming more competitive across a variety of economic sectors that account for 25% of total emissions. This trend is especially obvious in the electricity and transportation sectors, and it has opened up a slew of new business opportunities for those who get in early.

By 2030, zero-carbon solutions may be competitive in industries that account for more than 70% of world emissions.

Related posts on the SimTrade blog

▶ Anant JAIN The World 10 Most Sustainable Companies in 2021

▶ Anant JAIN Dow Jones Sustainability Index

▶ Anant JAIN United Nations Global Compact

▶ Anant JAIN Environmental, Social & Governance (ESG) Criteria

Useful resources

United Nations The Paris Agreement

United Nations What Is Climate Change?

United Nations How the Paris Agreement will help tackle the climate crisis (with Aidan Gallagher)- Within Our Grasp (video)

United Nations What is the ‘Paris Agreement’, and how does it work? (video)

About the author

The article was written in May 2022 by Anant JAIN (ESSEC Business School, Grande Ecole Program – Master in Management, 2019-2022).

Social Impact Bonds

by

Social Impact Bonds

Anant Jain

In this article, Anant JAIN (ESSEC Business School, Grande Ecole Program – Master in Management, 2019-2022) talks about Social Impact Bonds.

Introduction

Social impact bonds (also known as a social benefit goods or social bonds) are one-of-a-kind public-private partnerships that use performance-based contracts to fund effective social services. They are a type of financial security that provides capital to the government to fund projects that improve social outcomes while saving money. Impact investors provide capital to help high-quality service providers scale their operations. If and when the project achieves outcomes that generate public value, the government repays those investors.

Social impact bonds transfer the risk from the public sector to the private sector and further align project partners on the achievement of meaningful impact projects. For example, these projects can help low-income mothers have healthy births, reduce carbon emission, or support refugees through job training. In 2010, Social Finance UK issued the first ever social impact bond in the market. Over 160 social impact bonds have been issued in 28 countries, with more than 25 in the United States.

Purpose of Social Impact Bonds

The goal of social impact bonds is more than just to make money. The securities are designed to bring together the interests of various entities, such as governments, investors, social enterprises, and the general public, in order to develop effective solutions to public-sector problems.

Despite the fact that these securities are called bonds, they lack many of the characteristics of traditional bonds. Social impact bonds have a fixed term, but investors do not receive a fixed interest rate of return. Instead, the success of the project that was subsidized with the bonds is what determines whether the bonds are repaid or not.

If a project is successful, the government repays the investors by using the savings generated by the project. The investors, on the other hand, receive nothing if the project fails. As a result, social impact bonds carry a high level of risk for investors.

How Does a Social Impact Bond Work?

Social impact bonds are often differentiated from other fixed-income securities by the number of key players involved in the capital-raising process. It is further illustrated by Figure 1 and the steps involved are mentioned below.

Figure 1. Social Impact Bond Working Process.

Social Impact Bond Working Process

Source: Social Finance, UK .

1. Partner

The government determines the social issue and the goal by working with an intermediary, such as Social Finance, and high-performing service providers (organizations with a track record of success and evidence that their programs work) to achieve its goal.

2. Develop & finance

The project’s design, negotiation, and financial structure are all driven by Social Finance in collaboration with the government and the provider. Then, to provide upfront, flexible funding, the project raises capital from impact investors.

3. Deliver services

With ongoing support from Social Finance, the provider provides services to the target population, including governance oversight, performance management, course corrections, financial management, and investor relations.

4. Attain positive results

People in need can improve their lives by having healthy births, raising kindergarten-ready children, staying out of prison, and finding and keeping good jobs with the help of high-quality services.

5. Measure the outcomes

The impact of the project is measured by an independent evaluator using predetermined outcome metrics. If the project is a success, the government reimburses the project’s backers. The government, on the other hand, only pays based on the level of results achieved.

A Social Impact Bond in Practice

In 2010, the United Kingdom’s Peterborough Prison issued one of the world’s first social impact bonds. The bond raised £5 million from 17 social investors to fund a pilot project aimed at lowering short-term prisoner re-offending rates. Over the course of six years, the relapse or re-conviction rates of Peterborough inmates will be compared to the relapse rates of a control group of inmates.

The Peterborough Social Impact Bond was declared a success by the Ministry of Justice in 2017, with a 9 percent reduction in reoffending of short-sentenced offenders compared to a control group, exceeding the bond’s target of a reduction 7.5 percent. As a result, investors received a yearly return of 3%.

Related posts on the SimTrade blog

Useful resources

Social Finance, UK

Reducing reoffending in Peterborough

About the author

The article was written in May 2022 by Anant JAIN (ESSEC Business School, Grande Ecole Program – Master in Management, 2019-2022).

The World 10 Most Sustainable Companies in 2021

by

The World 10 Most Sustainable Companies in 2021

Anant Jain

In this article, Anant JAIN (ESSEC Business School, Grande Ecole Program – Master in Management, 2019-2022) talks about the World 10 Most Sustainable Companies in 2021.

Introduction

The Corporate Knights’ yearly list is a ranking of the 100 most sustainable companies. It is based on the analysis of companies with revenues over $1 billion (8,080 companies in 2021). This year marks the 17th year of the list.

This list is usually revealed during the World Economic Forum in Davos. The Davos Agenda is a ground-breaking gathering of world leaders to shape the values, policies, and alliances required in this difficult new environment. The World Economic Forum has been a trusted venue for leaders from business, government, international organizations, civil society, and academia to assemble at the start of each year to discuss crucial issues.

In 2021, the breakdown of the most sustainable firms by geographical areas is as follows:

  • 46 in Europe
  • 33 in North America
  • 18 in Asia
  • 2 in South America
  • 1 in Africa

The top 10 most sustainable corporations of 2021 are as follows:

1. Schneider Electric SE, France
2. Orsted A/S, Denmark
3. Banco do Brazil SA, Brazil
4. Neste Oyj, Finland
5. Stantec Inc, Canada
6. McCormick & Company Inc, United States
7. Kering SA, France
8. Metso Outotec, Finland
9. American Water Works Company Inc, United States
10. Canadian National Railway Co, Canada

We detail below the characteristic of each company in the dimension of sustainability.

1. Schneider Electric SE

Industry: Electrical Equipment
Location: France
Year Founded: 1836

Schneider Electric has been named the world’s most environmentally friendly firm. This European energy and automation multinational corporation was praised for its quick and consistent response to ESG – environmental, social, and governance – issues, moving up from 29th place in 2020.

Schneider Electric is helping to reduce CO2 emissions and the rise of the Earth’s temperature by focusing on innovative and renewable alternatives. Its efforts are assisting in the prevention of global warming and the production of ecologically friendly goods that improve energy access.

The core of Schneider Electric’s strategy, according to Chair and CEO Jean-Pascal Tricoire, is to build a sustainable business and organization. Schneider has long been committed to environmental issues, and it continues to raise the bar for itself, its customers, and its partners.

2. Ørsted A/S

Industry: Electricity Generation
Location: Denmark
Year Founded: 2006

After vowing to combat climate change with renewable energy, Ørsted was voted the world’s second most sustainable company. Despite dropping to second position in 2020, the Danish power company is still the world’s most sustainable energy provider, a title it has held for three years.

The corporation, which is also renowned as one of the top renewable energy generators, has switched its operations from fossil fuels to renewable energy and has set a goal of becoming carbon neutral by 2025.

Ørsted CEO Mads Nipper said the company’s strong placement in the Global 100 report underlines both its commitment to driving a successful and sustainable business and its resolve to become a catalyst for green energy change. He also stated that in order to be effective in the fight against climate change – and to stay in business – all businesses must adopt a sustainable business model.

3. Banco do Brazil SA

Industry: Financial Services
Location: Brazil
Year Founded: 1808

Banco do Brazil, Brazil’s, and Latin America’s largest bank by assets, is also one of the most sustainable companies. The 212-year-old bank aspires to be inclusive and contribute to digitally improving society by providing internet access and supporting education by fostering innovation and motivating entrepreneurs.

In 2020, the government-owned corporation was ranked ninth, but it has quickly risen through the ranks this year.

4. Neste Oyj

Industry: Oil and Gas Industry
Location: Finland
Year Founded: 1948

Neste is a global pioneer in sustainability, with products such as renewable diesel, sustainable aviation fuel, chemical recycling to reduce plastic waste, and raw material refining innovation. The Finnish company dropped from third to fourth place in a year, but it has been on the Corporate Knights Global 100 Index for the 15th year in a row, far longer than any other global energy company.

The company’s mission of making the world a better place for our children, according to Peter Vanacker, President and CEO of Neste, drives them to strive for greater heights every day. Many companies are constantly improving their sustainability programs, making it more difficult to make the list each year. More businesses are actively implementing sustainability into their operations, which is encouraging.

5. Stantec Inc.

Industry: Engineering, Architectural Design
Location: Canada
Year Founded: 1954

Stantec is not only one of the most ecologically responsible companies in the world, but it is also a leader in North America. Clean earnings and clean investment, which are goods and services with a demonstrated environmental and social impact, accounted for half of the company’s overall score.

Gord Johnston, President and CEO of Stantec, remarked that its remarkable track record on sustainability is the result of its people’s deep commitment and good leadership throughout the company’s global operations. Its teams are striving to improve sustainability in its own operations and aiding clients in developing and achieving sustainability goals.

6. McCormick & Company Inc.

Industry: Processed & Packaged goods
Location: U.S.A.
Year Founded: 1889

McCormick & Company is not just the world’s sixth most sustainable company, but it is also the leader in the food market. Since the index’s debut five years ago, the packaged and processed foods industry in the United States has advanced 16 points to its highest position.

According to Lawrence E Kurzius, Chairman, President of McCormick & Company, it has never been more important to work together for the future of flavor and to limit its impact on the environment. The company is dedicated to producing clean revenue, providing renewable energy projects, and making the transition to 100% circular packaging.

7. Kering SA

Industry: Luxury
Location: France
Year Founded: 1963

Gucci, Saint Laurent, Bottega Veneta, Ulysse Nardin, and Pomellato’s parent business are the only luxury brands to make the top 10 sustainable companies list.

When measured against 24 quantitative key performance indicators (KPIs), including resource management, people management, financial management, clean revenue and investment, and supplier performance, Kering maintained its strong position. In order to build the future of luxury, sustainability is promoted at every level of governance, from the Board of Directors to the operational managers.

Kering’s vow to protect the environment on which it relies, according to the CEO Dr. M Sanjayan, is a big step forward for the fashion business, and it offers a massive doorway for the luxury sector to influence the people and help rethink fashion and luxury goods.

8. Metso Outotec

Industry: Industrial Machinery
Location: Finland
Year Founded: 2020

Metso Outotec is ranked 8th on the Global 100 Index, a global leader in sustainable technology and services for the recycling, aggregates, and mineral processing industries. In order to have a good impact on the globe as a sustainable leader, the Finland-based firm has set a number of lofty goals, including reducing global warming to 1.5 degrees Celsius.

Piia Karhu, Senior Vice President Business Development at Metso Outotec, remarked that their customers in the aggregates and metals and minerals industries are focused on producing sustainable goods and services. They collaborate with their customers, partners, and communities to advance sustainable innovation.

9. American Water Works Company

Industry: Utilities, Water and Wastewater
Location: U.S.A.
Year Founded: 1886

Because of its leadership and transparency, American Water is one of the top ten sustainable firms. The largest publicly listed water and wastewater utility firm in the world, founded in 1886 and employing over 6,800 people, is based in the United States.

Despite serving 15 million people in 46 states, the company saves 12.5 billion liters of water each year through efficiency measures. It has also promised to reducing greenhouse gas emissions by 40% by 2025.

10. Canadian National Railway Company

Industry: Rail Transport
Location: Canada
Year Founded: 1919

The lone railway company on the list for 2021 was the Canadian National Railway. The railway conglomerate adheres to a global standard for sustainability activities, adhering to the UN Global Compact principles and the Sustainable Development Goals of the United Nations (SDGs).

Related posts on the SimTrade blog

▶ Anant JAIN Dow Jones Sustainability Index

▶ Anant JAIN Green Investments

▶ Anant JAIN Environmental, Social & Governance (ESG) Criteria

▶ Anant JAIN The Paris Agreement

Useful resources

General resources

Corporate Knights’ Global Ranking List

The Davos Agenda

Top 10 sustainable companies

#1 Schneider Electric SE, France

#2 Orsted A/S, Denmark

#3 Banco do Brazil SA, Brazil

#4 Neste Oyj, Finland

#5 Stantec Inc, Canada

#6 McCormick & Company Inc, USA

#7 Kering SA, France

#8 Metso Outotec, Finland

#9 American Water Works Company, USA

#10 Canadian National Railway, Canada

About the author

The article was written in May 2022 by Anant JAIN (ESSEC Business School, Grande Ecole Program – Master in Management, 2019-2022).

Dow Jones Sustainability Index

by

Dow Jones Sustainability Index

Anant Jain

In this article, Anant JAIN (ESSEC Business School, Grande Ecole Program – Master in Management, 2019-2022) talks about Dow Jones Sustainability Index (DJSI).

Introduction

The Dow Jones Sustainability Index (DJSI) was established in 1999 to honor publicly traded companies that excel in the field of sustainability. As of 2021, it includes 323 companies from a variety of industries that stand out for their outstanding environmental, social, and governance (ESG) performance.

The DJSI was created by S&P Dow Jones Indices (one of the world’s leading resources for benchmark and investable indices) and SAM (corporate sustainability assessment issued by S&P Global) to select the most sustainable companies from 61 industries, combining the experience of an established index provider with the expertise of a specialist in Sustainable Investing.

The indices act as a benchmark for investors who incorporate sustainability considerations into their portfolios, as well as a platform for investors who want to encourage companies to improve their corporate sustainability practices.

Sustainability

Generally speaking, sustainability can be defined as the ability to maintain or support a process over time. Applied to the planet, it refers to the prevention of natural resource depletion to maintain ecological balance for future generations. The term sustainable development was first coined and defined in the 1987 Brundtland Report, Our Common Future, published by the World Commission on Environment and Development of the United Nations (UN): “Development that meets the needs of the present without compromising the ability of future generations to meet their own needs.” In the business context, corporate sustainability is a comprehensive approach to managing operations while ensuring long-term environmental, social, and economic balance. It encompasses a company’s strategies and actions aimed at minimizing negative environmental and social impacts within its market. The sustainability practices of a firm or any organization are typically assessed against environmental, social, and governance (ESG) criteria.

Understanding DJSI

Companies that are included in the DJSI gain not only public recognition and a high level of acceptance from their stakeholders (for their best practices in this field), but they also become a benchmark for many other companies that aspire to be included in the index and want to improve their ranking to be among the best in the world. It is also a key tool for investors, who find these companies appealing and trustworthy, and value them for including policies like these in their strategy, which outperforms other organizations in terms of long-term profitability.

Being accepted into the Dow Jones Sustainability Index is a difficult task. Companies need to pass a rigorous assessment questionnaire with approximately 600 indicators that measure various criteria relating to their corporate governance, code of ethics and conduct, risk management, business, and providers in order to be included in this demanding ranking. Other environmental aspects are also investigated, such as the development of products and programs that are more environmentally friendly and promote efficiency, as well as initiatives aimed at defending human rights, encouraging talent retention and financial inclusion, and improving employee health and well-being.

S&P Global, the world’s largest index provider, is in charge of verifying each of the indicators using a questionnaire with 100 questions about the companies’ environmental, social, and governance performance. The businesses are then graded on a scale of one to one hundred points. Analysts at S&P Global also look at how companies break down public information in their communications with analysts and investors. Only those who achieve the highest ranking in their field of activity are invited to join the DJSI.

Example

MAPFRE is included in the Dow Jones Sustainability World Index for the third year in a row (from 2016 to 2019), with a total score of 77 out of 100. In the areas of customer relationship management, principles for sustainable insurance, social and environmental reporting, and financial inclusion, the company has improved its environmental and social rating and received the highest score (100 points).

MAPFRE has set more than 30 objectives for 2021 to address global issues such as climate change and inequality. It does so as part of its commitment to sustainability and in accordance with its Sustainability Plan 2019–2021, a roadmap that lays out a series of projects aimed at helping the company achieve carbon neutrality, become a leader in the circular economy, promote women’s leadership, and improve financial education, among other objectives.

Methodology

Based on the companies’ Total Sustainability Scores from the annual S&P Global Corporate Sustainability Assessment, the DJSI uses a transparent, rules-based component selection process (CSA). For inclusion in the Dow Jones Sustainability Index family, only the top-ranked companies in each industry are chosen. This process does not exclude any industries. The methodology used by S&P Global to build the DJSI index family is illustrated in Figure 1.

Figure 1. S&P Global methodology for the DJSI index family.
MSCI ESG Classification
Source: S&P Global.

As mentioned by S&P Global on its website, the DJSI is rebalanced quarterly and is reviewed each year in September based on the S&P Global ESG Scores resulting from the annual SAM CSA.

Index family

As shown in the following list, the Dow Jones Sustainability Index family includes global, regional, and country benchmarks:

  • DJSI World
  • DJSI North America
  • DJSI Europe
  • DJSI Asia Pacific
  • DJSI Emerging Markets
  • DJSI Korea
  • DJSI Australia
  • DJSI Chile
  • DJSI MILA Pacific Alliance

S&P Dow Jones Indices also offers DJSI Indices with exclusion criteria such as Armaments & Firearms, Alcohol, Tobacco, Gambling, and Adult Entertainment for investors who want to limit their exposure to controversial activities.

All DJSI indices are calculated and disseminated in real time, in both price and total return versions.

Related posts on the SimTrade blog

▶ Anant JAIN Environmental, Social & Governance (ESG) Criteria

▶ Anant JAIN MSCI ESG Ratings

Useful resources

Brundtland, G.H. (1987) Our Common Future: Report of the World Commission on Environment and Development.

S&P Global

MAPFRE

About the author

The article was written in May 2022 by Anant JAIN (ESSEC Business School, Grande Ecole Program – Master in Management, 2019-2022).

Implied Volatility

by

Jayati WALIA

In this article, Jayati WALIA (ESSEC Business School, Grande Ecole Program – Master in Management, 2019-2022) explains how implied volatility is computed from option market prices and a option pricing model.

Introduction

Volatility is a measure of fluctuations observed in an asset’s returns over a period of time. The standard deviation of historical asset returns is one of the measures of volatility. In option pricing models like the Black-Scholes-Merton model, volatility corresponds to the volatility of the underlying asset’s return. It is a key component of the model because it is not directly observed in the market and cannot be directly computed. Moreover, volatility has a strong impact on the option value.

Mathematically, in a reverse way, implied volatility is the volatility of the underlying asset which gives the theoretical value of an option (as computed by Black-Scholes-Merton model) equal to the market price of that option.

Implied volatility is a forward-looking measure because it is a representation of expected price movements in an underlying asset in the future.

Computation methods for implied volatility

The Black-Scholes-Merton (BSM) model provides an analytical formula for the price of both a call option and a put option.

The value for a call option at time t is given by:

Call option value

The value for a put option at time t is given by:

Put option value

where the parameters d1 and d2 are given by:,

call option d1 d2

with the following notations:

St : Price of the underlying asset at time t
t: Current date
T: Expiry date of the option
K: Strike price of the option
r: Risk-free interest rate
σ: Volatility of the underlying asset
N(.): Cumulative distribution function for a normal (Gaussian) distribution. It is the probability that a random variable is less or equal to its input (i.e. d₁ and d₂) for a normal distribution. Thus, 0 ≤ N(.) ≤ 1

From the BSM model, both for a call option and a put option, the option price is an increasing function of the volatility of the underlying asset: an increase in volatility will cause an increase in the option price.

Figures 1 and 2 below illustrate the relationship between the value of a call option and a put option and the level of volatility of the underlying asset according to the BSM model.

Figure 1. Call option value as a function of volatility.
Call option value as a function of volatility
Source: computation by the author (BSM model)

Figure 2. Put option value as a function of volatility.
Put option value as a function of volatility
Source: computation by the author (BSM model)

You can download below the Excel file for the computation of the value of a call option and a put option for different levels of volatility of the underlying asset according to the BSM model.

Excel file to compute the option value as a function of volatility

We can observe that the call and put option values are a monotonically increasing function of the volatility of the underlying asset. Then, for a given level of volatility, there is a unique value for the call option and a unique value for the put option. This implies that this function can be reversed; for a given value for the call option, there is a unique level of volatility, and similarly, for a given value for the put option, there is a unique level of volatility.

The BSM formula can be reverse-engineered to compute the implied volatility i.e., if we have the market price of the option, the market price of the underlying asset, the market risk-free rate, and the characteristics of the option (the expiration date and strike price), we can obtain the implied volatility of the underlying asset by inverting the BSM formula.

Example

Consider a call option with a strike price of 50 € and a time to maturity of 0.25 years. The market risk-free interest rate is 2% and the current price of the underlying asset is 50 €. Thus, the call option is ‘at-the-money’. If the market price of the call option is equal to 2 €, then the associated level of volatility (implied volatility) is equal to 18.83%.

You can download below the Excel file below to compute the implied volatility given the market price of a call option. The computation uses the Excel solver.

Excel file to compute implied volatility of an option

Volatility smile

Volatility smile is the name given to the plot of implied volatility against different strikes for options with the same time to maturity. According to the BSM model, it is a horizontal straight line as the model assumes that the volatility is constant (it does not depend on the option strike). However, in practice, we do not observe a horizontal straight line. The curve may be in the shape of the alphabet ‘U’ or a ‘smile’ which is the usual term used to refer to the observed function of implied volatility.

Figure 3 below depicts the volatility smile for call options on the Apple stock on May 13, 2022.

Figure 3. Volatility smile for call options on Apple stock.
Apple volatility smile
Source: Computation by author.

Excel file for implied volatility from Apple stock option

We can also observe that the for a specific time to maturity, the implied volatility is minimum when the option is at-the-money.

Volatility surface

An essential assumption of the BSM model is that the returns of the underlying asset follow geometric Brownian motion (corresponding to log-normal distribution for the price at a given point in time) and the volatility of the underlying asset price remains constant over time until the expiration date. Thus theoretically, for a constant time to maturity, the plot of implied volatility and strike price would be a horizontal straight line corresponding to a constant value for volatility.

Volatility surface is obtained when values for implied volatilities are calculated for options with different strike prices and times to maturity.

CBOE Volatility Index

The Chicago Board Options Exchange publishes the renowned Volatility Index (also known as VIX) which is an index based on the implied volatility of 30-day option contracts on the S&P 500 index. It is also called the ‘fear gauge’ and it is a representation of the market outlook for volatility for the next 30 days.

Related posts on the SimTrade blog

   ▶ All posts about Options

   ▶ Akshit GUPTA Options

   ▶ Jayati WALIA Brownian Motion in Finance

   ▶ Jayati WALIA Brownian Motion in Finance

   ▶ Youssef LOURAOUI Minimum Volatility Factor

   ▶ Youssef LOURAOUI VIX index

Useful resources

Academic articles

Black F. and M. Scholes (1973) “The Pricing of Options and Corporate Liabilities” The Journal of Political Economy, 81, 637-654.

Dupire B. (1994). “Pricing with a Smile” Risk Magazine 7, 18-20.

Merton R.C. (1973) “Theory of Rational Option Pricing” Bell Journal of Economics, 4, 141–183.

Business

CBOE Volatility Index (VIX)

CBOE VIX tradable products

About the author

The article was written in May 2022 by Jayati WALIA (ESSEC Business School, Grande Ecole Program – Master in Management, 2019-2022).

A quick presentation of the Private Equity field…

by

A quick presentation of the Private Equity field…

Louis DETALLE

In this article, Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023) explains what does an M&A daily life looks like.

What does Private Equity consist in?

Private Equity, represents fundamental and indispensable funding-support throughout the life cycle of the company.
Private equity consists in taking (minority or majority) stakes in the capital of (small or medium) companies which are generally unlisted on the stock exchange. It is therefore a method of financing companies in order to support them on the path to growth in the relatively short term. Indeed, the objective of the private equity fund is obviously to realize a capital gain at the exit, after 5 to 8 years in general, the time for the invested capital to generate a return on investment.

What are the main categories of Private Equity?

Venture capital

This type of capital investment is mainly aimed at small businesses/start-ups. Its target is to launch the activity of a company in the creation or start-up phase. Indeed, for a start-up, it is often difficult and premature to call on bank loans that follow very specific and very standardized covenants.

Development capital / growth capital

It aims at entering the capital of a company that has reached a certain maturity and profitability. The funds collected will then be used for internal and external growth: respectively the development of the company’s offers in order to develop its activities or the acquisition of competitors.

Turnaround capital

This type of capital investment aims at restructuring a company in difficulty. The call for bank financing having generally become impossible when the company experiences a major crisis, the turnaround capital fund will enter the capital to allow the company to reconnect with profitability and profits.

Transmission capital

This mode of capital entry is observed when a change of owner occurs. The objective is to ensure the gradual transition and preserve the profitability of the company. Traditionally, the LBO “leveraged buy-out” or the LMBO “leveraged management buy-out” is used, i.e. its buyout by the debt of a holding company constituted especially for the occasion.

What does an analyst in private equity work on?

The tasks of a Private Equity analyst are diverse and include, for example, the producing and challenging a business plan, modelling different scenarios and strategies in Excel. The analyst and the investment teams of the private equity teams thoroughly analyze the companies seeking for funding. They try to determine whether the projections of the seeked investment are reasonable and not overestimated. Indeed, bear in mind that private equity funds intend to fund companies trough equity. And as equity investors (shareholders) are reimbursed at last in the event of a bankruptcy, their work is to determine if the company will really generate growth with the capital at stake. That’s why deep sector-analysis are also required from a private equity analyst.

About the author

The article was written in April 2022 by Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023).

A quick presentation of the M&A field…

by

A quick presentation of the M&A field…

Louis DETALLE

In this article, Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023) explains what does an M&A daily life looks like.

What does M&A consist in?

Mergers & Acquisitions (M&A) is a profession that advises companies wishing to develop their external growth, i.e. growth through the acquisition of a company or through a merger with it. M&A mandates are therefore carried out on the side of the company that wishes to acquire another company, “buy-side”, or on the side of a company that wishes to be acquired, “sell-side”.

What does an analyst work on?

The tasks of an M&A analyst are diverse and include, for example, drawing up a business plan, modelling different scenarios and strategies in Excel, and drafting information memorandums (IMs) on the various deals in progress. All these skills are then widely used for the mergers and acquisitions of companies, in the development of their external strategy, in their financial evaluation or in the analysis of databases. Overall, M&A allows you to move into any sector of finance and this is part of the reason why it is so attractive.

Why does M&A jobs appeal so much to students?

First of all, it is the dynamic working atmosphere that investment banking enjoys that also attracts young graduates. M&A is indeed marked by a culture of high standards and maximum commitment, with highly responsive teams and extremely competent colleagues. Working in a quality team is very stimulating, and often makes it possible to approach the workload with less apprehension and to rapidly increase one’s competence. The remuneration is also much higher than in other professions at the beginning of a professional career for a young graduate and it progresses rapidly. Finally, it is also the exit hypotheses that attract young M&A analysts.

What are the main exits for M&A?

Most professionals who started out in M&A move on to other types of activities where experience in this sector is required. This is particularly the case in private equity. After advising companies on their growth and expansion projects, the young investment banker has all the tools needed to work in investment funds. The skills are indeed transposable to the financial and strategic questions that private equity funds ask themselves in order to obtain a return on investment.

Switching to alternative portfolio management (hedge funds) is also a possibility. Hedge funds can invest in different types of assets such as commodities, currencies, corporate or government bonds, real estate or others. As a former M&A analyst, you have the skills to analyse the market and determine the assets that seem to be the most appropriate and profitable.

Finally, some former M&A bankers switch to corporate M&A, which involves determining which companies or subsidiaries the company should buy or sell. This can be a very interesting area as you have the opportunity to follow the acquisition of a company from start to finish and therefore take a long-term view of the company’s strategy.

Related posts on the SimTrade blog

   ▶ Suyue MA Analysis of synergy-based theories for M&A

   ▶ Louis DETALLE How does a takeover bid work & how is it regulated?

   ▶ Raphaël ROERO DE CORTANZE In the shoes of a Corporate M&A Analyst

   ▶ Basma ISSADIK My experience as an M&A Analyst Intern at Oaklins Atlas Capital

   ▶ Antoine PERUSAT A New Angle in M&A E-Commerce

Useful resources

Décideurs magazine Rankings for M&A banks in France (league tables)

About the author

The article was written in May 2022 by Louis DETALLE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2023).

Warren Buffet and his basket of eggs

by

Warren Buffet and his basket of eggs

Rayan AKKAWI

In this article, Rayan AKKAWI (ESSEC Business School, Master in Strategy & Management of International Business (SMIB), 2021-2022) analyzes the two following quotes “Do not put all eggs in one basket” and “Put all your eggs in one basket and watch that basket” often used by Warren Buffet to describe his investment strategy.

“Do not put all eggs in one basket”

I particularly liked this quote first because it is said by the world’s greatest investor and one of the richest people on the planet, Warren Buffet. I aspire this man due to his great investment philosophy which is to invest in great businesses at value for money prices and then by using the “buy and hold strategy” keep the stocks over the long term. He has bought great brands such as Coca Cola, Microsoft, and American Express. Second, I like this quote particularly because it is dedicated to any person who has little or no knowledge in investment, so it is easy to implement.

Analysis

If we analyze the wealthiest people in the world, they are entrepreneurs who have created companies that grew exponentially in value. For example, Bill gates who is the founder of Microsoft (1975), Jeff Bezos who is the founder of Amazon (1994), and Mark Zuckerberg who is the founder of Facebook (2004). And as we continue to analyze these founders, we come to realize that they have made their wealth by putting all their eggs in one basket at least early in their lives. However, not all of us have this entrepreneurial spirit and business success such as these brilliant men. Thus, when Warren Buffet said “do not put all eggs in one basket” he was referring to an average person who has little knowledge in investments. Therefore, he advocates investment into index tracker or passive funds which have the benefit of low charges, better performance, and large diversification than most active managed funds. This involves a buy and hold strategy which keeps share dealing charges low. Thus, it is always recommended to have 80% of investments in passive funds which are low cost, predictable, and conservative funds and 20% of investments in satellite which usually involve higher charges with greater volatility and greater returns.

Another way of looking at it is the following. One might decide to invest a certain number of personal wealth in a new business or in crypto. This would be a risky type of investment because another competitor might release a better and more attractive or even more affordable version of the product or service. Eventually, this might put you out of business if a customer writes a bad review of your product or business or if the bitcoin value drops.

So before you invest more time and money in your business, consider how you can manage your risk. First, you must think about your risk tolerance which depends on your age and current financial obligations. Second, you need to keep sufficient liquidity in your portfolio by setting aside an emergency fund that should be equal to 6 to 8 months’ expenses. For ensuring that there is easy accessibility to emergency funds, you should have low-risk investment options like Liquid Funds and Overnight Funds in your accounts. Then you need to determine an asset allocation strategy that works which refers to investing in more than one asset class for reducing the investment risks and this strategy also provides you with optimal returns. You can invest in a perfect mix of key asset classes like Equity, Debt, Mutual Funds, real estate, etc. One of the asset allocation strategies is to invest in a combination of asset classes that are inversely correlated to each other. After you have found the best mix of asset classes for your portfolio, you can reduce the overall investment risk by diversifying your investment in the same asset class. Think about diversifying by offering more than one product or service. To avoid liquidity risk, it is always better to stay invested in blue chip stock or fund. Investors should check the credit rating of debt securities to avoid default risk.

“Put all your eggs in one basket and watch that basket”

At the same time, Warren Buffet believes that diversification makes little sense if a person doesn’t know exactly what he or she is doing. Diversification is a protection against ignorance and is for people who do not know how to analyze businesses. Sometimes it is enough to invest in two or three companies that are resistant to competition rather than fifty average companies due to less risk. That is why it is as critical for a person to invest in a company where its values and vision are similar to that of the investor and to be able to watch closely the performance of that business and its stocks.

Thus, Warren Buffet believes that it is extremely crucial to be able to “watch your basket” or your stocks closely to better understand the stock market. For example, when the stock market is going down, it is the best way to start buying stocks because businesses will be selling at a discount.

Why should I be interested in this post?

One would be interested to read this post because it introduces the basics of investing in stock markets for an average person who has little knowledge in investments or for a student studying business. As a student, it is crucial and important to be able to have at least a general idea of the basic rules of investments and especially those stated by one of the most famous investors in the world such as Mr. Warren Buffet. Whether you are interested in buying stocks yourself or whether you are not, as a business student, you might be asked about investments and the financial market one time in your life and knowing some useful information about investments will be impressive for you. It will allow you to understand the bigger picture of financial markets, give some recommendations for your family and friends, and help you invest yourself in the safest and most successful way.

Related posts on the SimTrade blog

   ▶ Youssef LOURAOUI Portfolio

   ▶ Youssef LOURAOUI Passive Investing

   ▶ Youssef LOURAOUI Active Investing

   ▶ Youssef EL QAMCAOUI The Warren Buffett Indicator

Useful resources

Berkshire Hathaway

About the author

The article was written in May 2022 by Rayan AKKAWI (ESSEC Business School, Master in Strategy & Management of International Business (SMIB), 2021-2022).

Big data in the financial sector

by

Big data in the financial sector

Rayan AKKAWI

In this article, Rayan AKKAWI (ESSEC Business School, Master in Strategy & Management of International Business (SMIB), 2021-2022) explains the role of big data in the financial sector.

Big data is a term used for contemporary technologies and methodologies that are used to collect, process, and analyze complex data. Today, data is being created at an exponential rate. In fact, and according to a 2015 IBM study, 90% of the data in the world has been created in the past two years. As big data gets bigger, it becomes even more important and essential for executives in the financial sector to stay ahead of the curve. Also, it is expected that data creation will continue to grow moving forward in time.

Big Data in The Financial Sector

For decades, financial analysts have relied on data to extract insights. Today, with the rise of data science and machine learning, automated algorithms and complex analytical tools are being used hand in hand to get a head of the curve in diferetn areas of the financial sector.

Fraud prevention

First, data has helped with fraud prevention such as identity theft and credit card schemes. Abnormally high transactions from conservative spenders and out of region purchases often signal credit card fraud. Whenever this happens, the card is automatically blocked, and a notification is sent out to the card owner. This protects users, insurance companies, and banks from huge financial loses in a small period. This also made things even easier and more practical avoiding the hassle of having to call and cancel the card. Data science comes in the form of tool like random forests that can detect a certain suspicion. In addition, and to lower the chance of identity theft, data has helped ease this process through 3D passwords, text messages, and PINT code which have backed up the safety of online transactions.

Anomaly detection

Second, data has helped the financial sector through anomaly detection. Data analysis is not only created to avoid a problem but also to detect it. For example, data today helps with catching illegal insider traders. To do so, data analysts created anomaly detection algorithms that can analyze history in trading patterns and thus detect and catch abnormal transactions of illegal traders.

Customer analytics

Third, data has helped with improving customer analytics. Data analyzes previous behavioral trends of consumers based on historical transactions and then makes future predictions of how consumers are likely to act. With the help of socioeconomic characteristics, we can create clusters of consumers and group customers based on how much money we expect to gain or lose from each client in the future. Following that, we can come up with decisions to focus on a certain type of clients to make profits and cut on other customers to make savings. Thus, financial institutions minimize human errors by utilizing data science. To achieve that, first, by identifying uncertain interactions and then monitor them going forward. Finally, prioritizing the investments most vulnerable at a given time. For example, banks use this approach to create adaptive real risk score time models to identify risky clients and those who are suitable for a mortgage or a loan.

Algorithmic trading

Fourth and most importantly data has created algorithmic trading. Machines make trading based on algorithms multiple times every second with no need for approval by a stand-by analyst. These trades can be in any market and even in multiple markets simultaneously. Thus, algorithmic trading has mitigated opportunity costs. Thus, there are algorithmic rules that can help in identifying if there is a need to trade or not to trade and reinforces business models where errors are highly penalized and then adjust hyper parameters. We can see algorithms that exploit arbitrage opportunities where they can find inconsistencies and make trades which can cause problems. The huge upside is that it is high frequency trading; whenever it will find an opportunity to make a trading, it will. However, the downside is that imprecision could lead to huge losses due to lack of human supervision. That is why sometimes human interventions are needed.

Conclusion

Thus, we can say that data has become the hottest commodity that results in getting an edge over competition. Financial institutions spend a huge amount of money to get exclusive rights to data. By having more information, they can construct better models. The most valuable commodities are not analysts but the data itself. That is how the data science has revolutionized finance.

Characteristics of Big Data

When talking about Big Data, four main characteristics need to be considered to understand the why Big Data plays a transformational role in the financial sector: volume, variety, velocity, and value.

Volume

First, the amount also known as volume of data being produced on daily basis by users has been increasing exponentially by users. This large output of data has helped create Zettabytes (1012 Gigabyte) and Yottabytes (1015 Gigabyte) of datasets in which companies can benefit by extracting knowledge and insights out of it. However, this amount of data cannot be processed using regular computers and laptops. Since they would require a lot of processing power.

Variety

Second, as the massive amount of data is being generated by multiple sources, the output of this data is unstructured making it hard to organize the data extract insights. Raw data extracted from the source without being processed does not provide any value to business as it does provide stakeholders with the ability to analyze it.

Velocity

Third, to address the issue of processing technological advancements have brought us to the tipping point where technologies such as cloud computing have enabled companies to process this large amount of data by utilizing the ability to share computational power. Furthermore, cloud platforms have not only helped in the processing part of data but by the emergence or cloud solution such as data lakes and data warehouses. Businesses are able to store this data in its original from to make sure that they can benefit from it.

Value

Finally, this brings us to the most important aspect of Big Data and that in being able to extract insights and value out of the data to understand what it is telling us. This process is tedious and time consuming however with ETL tool (Extract Transform Load) the data in its raw format is transformed so that standardized data sets can be produced. Insights can be extracted through Business Intelligence (BI) tools to create visualization that help business decisions. As well as predictive artificial intelligence models that help business predict when to take a strategic decision. In the case of financial markets, these decisions are when to buy or sell assets, and how much to invest.

Challenges Solved by Big Data in the Financial Industry

Utilizing Big Data in the finance industry presents a lot of benefits and helps the industry to overcome multiple challenges.

Data Quality

As previously mentioned, the multiple data sources present a huge challenge from a data management standpoint. Making it an ongoing and a tedious effort to maintain the integrity and the reliability of the records collected. Therefore, adding information processing systems and standardizing the data gathering and transformation processes helps improve the accuracy of the decision-making process, especially in financial services companies where real-time data enables fast decision making and elevates the performance of companies.

Data Silos

Since financial data comes from multiple sources (applications, emails, documents, and more), the use of data integration tools help simplifies and consolidate the data of the institution. These technologies facilitate processes and make them faster and more agile, which are important characteristics in the financial markets.

Robo-Advisory

Big Data and analytics have had a huge impact on the financial advisory sector. Where financial advisors are being replaced by machine learning algorithms and AI models to manage portfolio and provide customers with personalized advice and without human intervention.

Why should I be interested in this post?

This article is just an eye opener on the trends and the future state of the financial industry.

Like many other industries, the financial sector is becoming one of the most data driven field. Therefore, as future leaders it is vital to keep track and push towards data driven solutions to excel and succeed within the financial sector.

Related posts on the SimTrade blog

   ▶ All posts about Financial techniques

   ▶ Louis DETALLE Understand the importance of data providers and how they influence global finance…

   ▶ Louis DETALLE The importance of data in finance

   ▶ Louis DETALLE Reuters

   ▶ Louis DETALLE Bloomberg

Useful resources

The Future of Cognitive Computing

Five Ways to Use RPA in Finance

About the author

The article was written in May 2022 by Rayan AKKAWI (ESSEC Business School, Master in Strategy & Management of International Business (SMIB), 2021-2022).

Fiche Métier : Térsorier

by

Fiche Métier : Térsorier

Emma LAFARGUE

Dans cet article, Emma LAFARGUE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2024) décrit le métier de trésorier.

Que fait un trésorier ?

Le trésorier est la personne en charge des différents flux monétaire de l’entreprise. C’est lui qui gère la répartition et la distribution des liquidités (paiement des prestataires, rémunération des salariés) et qui veille à la stabilité financière. Il doit s’assurer que l’entreprise dispose d’un fonds de roulement suffisant pour son fonctionnement quotidien, c’est lui qui place l’argent et décide du financement et des investissements (en lien avec la direction stratégique)

Concrètement, ses missions au quotidien sont de :

  • Contrôler les dépenses et recettes de l’entreprise
  • Tenir le registre des différents mouvements financiers
  • Gérer les liquidités, c’est-à-dire veiller au remboursement des emprunts et à la rentabilité des investissements
  • Etudier les risques liés aux perspectives de placement

Avec qui travaille un trésorier ?

Le trésorier est en lien constant avec la direction de l’entreprise afin de décider de la stratégie de financement, de se mettre d’accord sur les investissements et les placements à effectuer.
Le trésorier est également le principal interlocuteur des banques et des investisseurs.

Enfin, il travaille en interaction avec les comptables, en charge de l’aspect technique des transactions financières : ils enregistrent les opérations et formulent des déclarations. Le trésorier quand-à-lui, est chargé des fonds directement.

Combien gagne un trésorier ?

Un trésorier gagne entre 3 700€ et 6 000€ brut par mois. Cependant, le salaire d’un trésorier junior varie entre 36 000€ et 48 000€ par an (source : cadremplois.fr 2021)).

Quel positionnement dans la carrière ?

Il est possible d’être trésorier junior. Cependant, les entreprises privilégient les personnes avec de l’expérience, c’est-à-dire ayant déjà exercé des fonctions de contrôleur de gestion ou comptable trésorerie.
Le trésorier peut aspirer à une belle évolution de carrière. Après 5 années, il peut bénéficier d’opportunités et évoluer en tant que directeur financier, responsable du service administratif, chef trésorier ou responsable trésorerie groupe.

Quelle formation ?

Pour être trésorier, un bac +5 est nécessaire en finance, commerce, gestion ou comptabilité.
Les formations peuvent donc être une école de commerce avec spécialisation en finance ou trésorerie ou un diplôme supérieur de comptabilité et de gestion (DSCG). Ces deux formations sont proposées par l’ESSEC : le DSCG peut être passé en parallèle et la spécialisation en finance se fait par le Corporate Finance Track disponible à Cergy et Singapour.

Lien avec le cours et concepts clés :

Pour être trésorier, il faut avoir une très bonne connaissance des différentes normes IFRS (International Financial Reporting Standards), du droit des affaires ainsi que toutes les notions de comptabilité et finance (Compte de résultat, Bilan, Tableau de financement, flux de trésorerie, fonds de roulement etc.)

Autres articles sur le blog SimTrade

   ▶ All posts about Professional experiences

   ▶ Anna BARBERO Career in finance

   ▶ Alexandre VERLET Classic brain teasers from real-life interviews

Resources utiles

Association Française des Trésoriers d’Entreprise

Cadremplois.fr Trésorier

A popos de l’auteure

Cet article a été écrit en Mai 2022 Emma LAFARGUE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2024).

Mon expérience en contrôle de gestion chez Chanel

by

Mon expérience en contrôle de gestion chez Chanel

Emma LAFARGUE

Dans cet article, Emma LAFARGUE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2024) partage son expérience en contrôle de gestion chez Chanel.

Présentation de l’entreprise

Chanel est une entreprise française de haute couture, prêt-à-porter, accessoires, parfums et autres produits de luxe. Elle a été créée en 1910 par Gabrielle Chanel. La maison appartient aujourd’hui à Alain et Gérard Wertheimer et son siège est basée à Neuilly. La maison est connue pour ses produits tels que le parfum numéro 5 créé en 1921, le tweed ou encore les sacs. Chanel est une des rares entreprises du luxe qui n’est pas côté en bourse. Son chiffre d’affaires s’élève à 10 milliards de dollars en 2020.

Logo de l’entreprise Chanel
Logo Chanel
Source: Chanel

Mon poste et qu’est-ce que le contrôle de gestion ?

Le contrôleur de gestion est la personne chargée de contrôler les budgets de l’entreprise en lien avec la mise en œuvre de la stratégie de l’entreprise.

De mon côté, j’étais au service de contrôle de gestion « Reporting et Budget » au sein de la division Mode de chez Chanel (il existait aussi d’autres service de contrôle de gestion comme la gestion des stocks). Mon périmètre était très large, je gérais tout d’abord les budgets de fonctionnement des équipes (sans la masse salariale qui était gérée par un autre employé), cela concerne donc les déplacements, voyages, séminaires, intérims et différents frais consultants. J’étais en charge de toutes les équipes de la division mode : le digital, la communication, le service clients (CRM pour customer relationship management), les sessions d’achats, les opérationnels ainsi que les équipes produits.

Je gérais également les coûts de collection c’est-à-dire les coûts de création de tous les prototypes destinés aux défilés et les budgets des sessions d’achats, soit le moment après le défilé durant lequel les directeurs des boutiques monde se rendent au siège pour choisir quelle pièce sera présente dans quelle boutique.

Enfin, je m’occupais des budgets de la logistique (supply chain) et des centres de distribution (retail). Ce volet passait par une partie « Suivie de projet » qui concernait un nouveau centre de distribution. Il fallait donc suivre la mise en fonctionnement de ce centre, en particulier les dépenses de de fonctionnement (OPEX pour operational expenses) et les investissements (CAPEX pour capital expenditures).

Concrètement, durant mon stage, j’exerçais différentes missions :

  • Lors des clôtures mensuelles : mise à jour mensuelle des fichiers de suivi des coûts par l’extraction des données de la comptabilité et consolidation dans nos fichiers de suivi et tableaux de bord
  • Travail d’analyse : traitement des données, analyse des écarts existant entre les chiffres de prévisions et les chiffres réalisés
  • Reporting aux différentes équipes pour les tenir au courant de l’avancée dans leurs budgets

La partie la plus intéressante de mon stage, selon moi, a été l’élaboration des budgets pour l’année suivante. L’objectif de ce travail est d’estimer les dépenses pour l’année suivante afin qu’elles soient validées par la Direction Financière et la Direction Générale.
L’élaboration des budgets passe par des réunions avec toutes les équipes opérationnelles afin de définir leurs besoins, comprendre leurs différents projets et les estimer.

L’élaboration des budgets permet au contrôleur de gestion de rester informé des différents projets que mènent les équipes pour pouvoir ensuite suivre au plus près leurs dépenses l’année suivante.

Au niveau opérationnel, le contrôleur de gestion est donc en relation directe avec toutes les équipes opérationnelles dont il a la charge, avec le service de comptabilité qui est chargé d’enregistrer et d’imputer les factures et donc les différents postes de coûts. Le contrôleur de gestion est aussi en relation avec le responsable du contrôle de gestion et le directeur financier.

Compétences et connaissances requises

Le travail se fait principalement sur Excel, ainsi, il faut maîtriser les principales commandes : recherche (recherche H ou V dans les feuilles Excel), somme.si (sommes conditionnelles), TCD (tableaux croisés dynamiques), etc.

Il faut avoir des connaissances sur l’entreprise, le secteur dans lequel on évolue et les différentes équipes avec qui on est en contact de manière régulière pour établir les budgets. La connaissance de chaque équipe est importante car l’activité peut différer beaucoup d’une équipe à l’autre (le suivi des budgets pour les coûts de collection est totalement différent de celui effectué pour la logistique).

Il faut également des connaissances dans le domaine financier : savoir analyser un compte de résultat, comprendre les différentes notions comptables telles que les immobilisations et amortissements.

Un esprit d’analyse et de synthèse sont aussi nécessaires pour effectuer les reportings mensuels aux différentes équipes.

Enfin, les qualités relationnelles sont indispensables car le contrôleur de gestion est en constante interaction avec les autres services de l’entreprise.

Autres articles sur le blog SimTrade

   ▶ All posts about Professional experiences

   ▶ Anna BARBERO Career in finance

   ▶ Chloé POUZOL Mon expérience de contrôleuse de gestion chez Edgar Suites

Resources utiles

Chanel

Association Nationale des Directeurs Financiers et de Contrôle de Gestion (DFCG)

A propos de l’auteure

Cet article a été écrit en mai 2022 par Emma LAFARGUE (ESSEC Business School, Grande Ecole Program – Master in Management, 2020-2024).